In briefDetecting data distribution shifts in drone remote sensing images with machine learning tools
SummaryThis paper examines how well machine learning models on drones can detect when the data they receive differs from their training data, a common issue in remote sensing. The authors run an empirical case study comparing three open-source detection tools: Alibi-Detect, NannyML, and PyTorch-OOD. Using cross-dataset remote sensing shifts, they measure both detection effectiveness and computational latency. The results show differences in detection behavior and processing cost, revealing trade-offs between detection capability and response time.
In briefMobile AI Factory: Automatically Building Custom Small Language Models for Phones
SummaryThis paper presents Mobile AI Factory, an agentic system that builds a fine-tuned, quantized small language model for a given task and phone. An orchestrator LLM decides whether to tune hyperparameters, mine real data, or generate synthetic examples, while fixed rules roll back changes that reduce accuracy. The system was evaluated on ten tasks and four Android phones.
In briefDark Matters: VAE-Aware HDR Encoding for Converting SDR to HDR Video
SummaryThis paper addresses reconstructing high dynamic range (HDR) video from standard dynamic range (SDR) footage, where clipped highlights and crushed shadows must be recovered. The authors find that common log-like encodings suppress chromatic differences in low-luminance areas, causing collapse in the frozen VAE latent space. They propose EquivFlow, a compact, invertible exposure-equivariant encoding that works without modifying the VAE. Integrated into an SDR-to-HDR video diffusion model, it improves chromatic fidelity across the tested settings while keeping reconstruction fidelity competitive.
In briefEvaluating compositional generalization in Transformers, focusing on composition equivalence and module coverage.
SummaryThis paper systematically studies whether Transformer models can generalize to unseen combinations of known modules after training on other combinations. It identifies composition equivalence and module coverage in train-test splits as key factors strongly affecting performance. The authors show that apparent gains of direct models mainly come from exploiting equivalent module sequences, and that performance falls as module coverage shift grows. A case study evaluates Llama3-8B on code-output prediction in LiveCodeBench.
Purva Pruthi, Andrew Yuan, Alexander D'Amour et al.
In briefReasoning-aware relational representation learning for open-vocabulary scene graph generation (RaRe)
SummaryThis paper addresses open-vocabulary scene graph generation, where models must recognize visual relationships beyond the training vocabulary, and notes that existing methods often depend on dataset-specific object-predicate co-occurrence patterns. It proposes RaRe, which turns MLLM-generated chain-of-thought rationales into structured relation embeddings, aligning them with textualized relation descriptions via self-supervised contrastive learning and refining generation with a specificity-oriented reinforcement learning objective. Experiments on VG150 and PSG show improved category-balanced recognition over existing baselines.
In briefSimultaneously Identifying Near-Optimal Regions in Continuous Simulation Optimization: Minimax Complexity and Adaptive Sampling
SummaryThe paper studies how to identify, on a continuous design space, the regions of near-optimal designs for all tolerances up to a maximum T, with region estimation error at most η. It gives a lower bound on the number of simulation evaluations any procedure requires. It proposes SASORI, which returns an optimal point estimate and the regions for all tolerances from the same evaluations, matching the lower bound up to logarithmic factors.
WhyThe authors formulate simultaneous near-optimal region identification across all tolerances, derive a minimax lower bound, and report an algorithm matching it up to log factors.
In briefWhether reconstruction helps event-camera person triggers: a comparative study
SummaryThis paper compares ways of turning event-camera data into inputs that a general visual model can use as a recording trigger. It evaluates direct grayscale accumulation, two temporal encodings, and two intensity reconstruction methods, including E2VID and FireNet+, on 192 windows from DSEC recordings. Reconstruction with E2VID detected more people than the direct encodings while producing fewer background false positives. The authors also discuss the trade-off between this detection benefit and the cost and false starts of continuous triggering.
In briefWoW: A Large-Scale World Knowledge Corpus for Training Large Language Models Across the Full Lifecycle
SummaryThis paper introduces WoW (World Knowledge), a large-scale dataset built for full-cycle training of foundation models. Its construction is guided by an Educational Knowledge Graph that organizes systematic collection and completion of educational modules across all levels. The authors report that this version improves systematicity, quality, and scale over earlier versions, and adds multimodal signals. Experiments show gains in model performance across diverse benchmarks, and an interactive platform for graph-based data selection and retrieval is also provided.
Tianze Sun (1), Yongyao Duan (2), Sam Chen (2) ((1) Harbin Institute of Technology et al.
In briefDetecting When Hands Touch and Release Objects in First-Person Video
SummaryThis paper addresses detecting hand-object contact transitions in egocentric video, localizing when a hand first touches an object and when contact is released. The authors propose a unified framework that jointly models the hand and object, using a transition-aware event spotting approach that needs no external object or interaction detectors at inference. They introduce the ContactTransition dataset of 4,021 egocentric videos with touch and release annotations. Experiments show the method outperforms strong two-stage and end-to-end baselines, and the detected transitions serve as useful temporal anchors for downstream hand-object reasoning.
In briefPrivacy-Preserving Statistical Inference for Pairwise-Comparison Rankings
SummaryThis paper develops methods for score and rank inference under the Bradley-Terry model with edge differential privacy, motivated by ranking large language models from human pairwise comparisons. It establishes finite-sample error bounds for an objective-perturbed estimator and gives asymptotically valid confidence intervals for individual scores and pairwise differences. It also proposes a bootstrap procedure for simultaneous rank inference, and the methods are evaluated by simulation and applied to ranking language models for creative writing.
In briefExact sample requirements for learning KL-regularized reinforcement learning policies
SummaryThis paper studies how many samples are needed to learn policies in KL-regularized reinforcement learning, the objective behind maximum-entropy RL and RL from human feedback. For discounted MDPs, it shows that a plug-in estimator reaches an epsilon-optimal policy with a stated sample bound, and that no algorithm can do better under certain conditions. The authors also derive the exact asymptotic constant of the estimator and an optimal sample allocation.
WhyThe authors give tight sample complexity bounds for KL-regularized RL, improving the H^5 horizon dependence and adding a temperature-dependent lower bound, checked by simulations on small MDPs.
In briefUsing a Psychophysical Attention Distribution to Improve Gaze-Based Reward Modeling
SummaryThis paper examines how human eye-tracking data can be used to train reward models for language models, so that model outputs better match human preferences. The authors model visual attention as a spatial distribution that decreases with distance from the current gaze point and include it in reward model training. Experiments show higher preference prediction accuracy than a baseline, and some results qualitatively match established findings on human attention during reading.
In briefDeep Personalization: Continuous Context Observation for Local Proactive Agents
SummaryThis paper introduces deep personalization, a closed-loop approach in which a desktop proactive agent, Coco, uses a separate Observer Agent to monitor user activity, retain compact long-term context, and learn when to offer help from implicit feedback such as engagement and dismissal. The authors develop on-device strategies so that raw activity data need not leave the machine. They also build OSWorld-Persona, a persona-conditioned benchmark created from existing computer-use trajectories, where adaptation raises macro F1 from 0.12–0.19 to 0.35–0.48. In a week-long user study, Coco's macro-averaged F1 rose from 0.199 to 0.515.
In briefWhen Reasoning Looks Like Hidden Understanding: How Chain-of-Thought Fosters Anthropomorphism and Deference to AI
SummaryThis paper examines why Chain-of-Thought reasoning in generative AI is often mistaken for a transparent explanation. The authors argue that treating language patterns as evidence of understanding encourages users to anthropomorphize AI and defer to it in decisions. They use medicine as an example and conclude that explainability through Chain-of-Thought is problematic, recommending that asking models for user-facing reasons be discouraged and that AI literacy training warn against it.
Oliver Lack, Lana Tikhomirov, Humphrey Thompson et al.
In briefSafeRoute-MAS: Risk-Calibrated Message Routing for Multi-Agent Scientific Reasoning
SummaryThe paper proposes SafeRoute-MAS, a method that uses only pre-integration information to decide whether to keep all specialist messages or remove one. On a sealed cache of 5,328 committees, its compatible operating point gains about +1.80 percentage points while removing 12.76% of message tokens. Controlled integrations on a 458-committee sample did not establish a selector advantage, and the authors propose a numerical qualification with Keep-All fallback, applied retrospectively rather than validated prospectively.
In briefCamera3R: a method that lets 3D reconstruction models work with different camera types
SummaryThis paper proposes Camera3R, a low-rank adaptation framework that extends 3D reconstruction networks pretrained on perspective images to other camera models. The authors report that adding only 0.5% more parameters and under a day of training adapts the model to non-perspective cameras. The original performance on perspective inputs is said to be fully preserved. Code is publicly available.
In briefSLIC: A method that fine-tunes small language models with reinforcement learning for multi-turn analog circuit optimization
SummaryThis paper introduces SLIC, a reinforcement fine-tuning framework that trains a 4B-scale, locally deployable language model for closed-loop analog circuit optimization. The authors also release an open benchmark of 41 amplifier topologies with standardized evaluation protocols. SLIC-Random reaches 87.1% four-specification Pass@1 within eight turns and transfers to unseen technology nodes and a commercial PDK without additional fine-tuning.
In briefFermat ProbCover: Coverage-Based Active Learning Using Fermat Distance Graphs
SummaryThis paper proposes PC-Fermat, an active learning method that selects examples for labeling by replacing Euclidean coverage neighborhoods with neighborhoods defined by a data-adaptive Fermat distance. To make computation practical, the authors build a weighted Euclidean neighborhood graph and apply a truncated shortest-path algorithm, and they give sufficient conditions under which this preserves the Fermat coverage of the complete graph. Experiments on eight classification datasets across labeling budgets, with emphasis on very low budgets, show PC-Fermat outperforms several existing methods on most datasets.
In briefForeSpark: Joint Draft and Verification Budget Scheduling for Parallel Speculative Decoding
SummaryThis paper proposes work-conserving speculation (WCS), a design that jointly allocates draft and verification budgets for each request in LLM serving, and presents ForeSpark as its prototype. ForeSpark uses a context-aware pre-draft estimator, a lightweight joint scheduler, and a post-draft budget distribution to choose both lengths. Implemented on vLLM, it reportedly improves throughput by up to 1.77x over DSpark and 2.49x over autoregressive decoding, without changing the output distribution.
In brief"Inherited Weights": Measuring Tokenization Premiums and Sovereignty Costs of Language Models for the Global South
SummaryThe paper argues that most language models used outside the US and China are fine-tuned from a few pretrained families, inheriting properties such as vocabulary, knowledge and cultural alignment from their base. Using 27 articles of the Universal Declaration of Human Rights, the authors measure tokenization premiums across 31 languages and 7 tokenizers released in 2023-2024. They also propose an eight-test black-box sovereignty audit that buyers can run without access to model weights.
In briefKernel Machines Explain In-Context Learning Without Feature Learning
SummaryThis paper studies memorization versus generalization in in-context learning (ICL) using rotation-invariant kernel machines on the linear regression task of Raventos et al. (2023). Decomposing the task in the kernel eigenbasis, the authors show that as sample size grows, models learn higher-degree components of a memorizing solution, and the generalizing solution is a low-degree truncation of it. The results suggest feature learning is not essential for ICL, and transformers trained on ICL show memorization consistent with this theory.
In briefOff-target posterior mass: a training-free metric for diagnosing fine-tuning gains in spoken language diarization
SummaryThis paper proposes Off-target Posterior Mass (OTPM), a diagnostic metric computed from posterior language identification probabilities. It is used to forecast how much performance improvement fine-tuning a spoken language diarization (SLD) probe will bring. OTPM correlates positively with probe gains, with coefficients of 0.56 on unseen synthetic bilingual data and 0.23 on real-world corpora. The results suggest OTPM is a promising diagnostic metric for SLD.
Zhuoyan Tao, Aditya Kommineni, and Shrikanth Narayanan
In briefBioMem: A Study of Agentic Memory for Biomedical LLM Agents
SummaryThis paper systematically studies how agentic memory systems retain, retrieve, update, and use biomedical information in LLM agents. The authors evaluate five memory approaches across four LLMs in biomedical knowledge reasoning, clinical prediction, and long-term conversational memory. They also propose HMRU, a training-free framework that combines hybrid memory retrieval with task-aware utilization, reporting an average 8.6% relative improvement and a 24.9% reduction in conversational answer errors.
In briefP2Echo: One model that segments heart structures across echo views using text prompts
SummaryThis paper presents P2Echo, a single model that segments diverse cardiac structures across multiple standard echocardiographic views, controlled by structured view-aware text prompts. Its core is a Dynamic Image-Text Alignment (DyITA) module, which uses a differentiable routing mechanism and token differential operators to modulate decoder features according to the requested view and target structure. Experiments on a large internal dataset and three external datasets show that P2Echo matches or exceeds view-specific and universal models at a fraction of their computational cost.
Moein Heidari, Alvin Kimbowa, Mahdi Tabatabaei et al.
In briefA zero-cost uncertainty measure for LLM outputs, derived from particle decoding
SummaryThe paper shows that Sequential Monte Carlo decoding for power sampling already estimates the normalizing constant of the power distribution, which yields a sequence-level Rényi entropy for an LLM's completions at essentially no extra cost. It proves that orders above one relate to each step's most likely token, while the order-1/2 entropy accumulates per-step search effort, and reports this as the best zero-cost selective-prediction signal in 10 of 13 settings. On MATH500 it outperforms log-probability in AUROC, and a two-pass policy matches 64-particle accuracy with 34.2% fewer particles.
WhyThe authors derive sequence-level Rényi entropy from SMC power-sampling normalizers at zero extra cost, with a chain rule; the abstract reports AUROC gains over log-probability on MATH500 and a two-pa
Erfan Baghaei Potraghloo, Milad Heidari Sorkhehdizaj, Shahab Rahimirad et al.
In briefPlacing LLM Lookup Tables by Per-Layer Prefetch Deadlines
SummaryThis paper examines where to store n-gram lookup tables used by a large language model, based on how much time each layer leaves for reading them. Row indices depend only on input tokens, so reads can begin at each step and overlap earlier layers, and the usable window differs widely between layers. The authors present SlackTier, a table tier design for SGLang that places tables on NVMe or reads them over RDMA and splits a small GPU row cache across layers, and they report changes in time per output token and host memory use.
In briefBursts That Keep Hurting: Persistent Slowdown in Prefix-Cached LLM Agent Serving
SummaryThis paper studies a failure mode in serving LLM agents with prefix caching, where a brief burst of extra requests pushes a server into a degraded state for a long time, with low cache hit ratio, about half throughput, and a growing backlog. A matched server without the burst stays healthy. The authors give a workload-balance condition for when the degraded state can drain, find that throttling new admissions helps little, while suspending admitted sessions against a capacity limit recovers service within minutes. A budgeter that bounds active sessions' resident KV demand to the cache pool prevented the failure in all tested runs.
WhyThe authors report a metastable degradation in prefix-cached agent serving after bursts and a KV-budgeting method that prevented it in tested runs.
In briefSteerDistill: Quantizing LLM Agents via Closed-Loop Teacher Steering
SummaryThis paper addresses how low-bit quantization cuts the inference cost of LLM agents but can reduce task success. The authors propose SteerDistill, where a full-precision teacher guides a quantized student to revise failed responses and refines that guidance using feedback from the student's execution. The student is then trained without guidance to internalize these corrections, enabling unassisted inference. Across five agentic benchmarks and three models, the method outperforms the strongest quantized baseline and stays within one point of BF16.
In briefLiving Constitutions: A participatory method for writing AI value rules for sovereign models
SummaryThis paper examines how to create the plain-text value rules used in Constitutional AI for sovereign foundation models. The authors argue that such a constitution should be developed as an evolving, participatory process, similar to citizen science, and propose five principles: iterative, modular, justifiable, positional, and legitimate. They suggest labeling provisions as settled, contested, or underdetermined so disagreement is recorded. Using agent-based modelling, they report that the process remains robust when 50% of citizens are bad actors, assuming 75% of the deliberative body has good intent.
Stine Lyngs{\o} Beltoft, Jonas Vistrup, Peter Schneider-Kamp et al.
In briefSpelling-to-sound predictability at two grain sizes, with English lexicon size matched
SummaryThe paper measures how predictably spelling maps to pronunciation, using conditional entropy at two unit sizes: aligned spelling-sound chunks and syllables. It analyses word lists in twelve alphabetic writing systems, reporting five in full, where English shows the most uncertainty and Finnish the least. A matched 20,000-word English sample is used to show that lexicon size must be equalised for fair comparison. The authors state that these estimates describe the sampled writing systems, not readers.
In briefDose Makes the Poison: Exposure-Aware Evaluation of Generative Drug Design
SummaryThis paper argues that generative models for drug discovery often select candidate molecules based only on predicted potency and toxicity, overlooking how those properties translate into drug exposure at a given dose. The authors use physiologically based pharmacokinetic (PBPK) simulations to re-evaluate 60 shortlisted molecules from three PARP1-targeted generative pipelines. Seven molecules failed to reach sufficient target exposure, 28% of the rest showed predicted safety risks at their required dose, and 85% of candidates with an achievable dose were outperformed by molecules overlooked in static selection.
In briefThe AI Era's Lines of Code? A Case Study on What an Engineer's Token Spend Measures
SummaryThis study links five weeks of coding-agent token usage to the commits of 404 JetBrains engineers and analyzes a survey of 412 employees. Token spend varied mostly between engineers, and the highest spenders stayed largely the same over the period. The authors conclude that token spend measures how much agents are used across many kinds of work rather than the outcome or value of that work, and recommend using it at most for resource accounting.
In briefPlumbline: A system that validates LLM training correctness when loss values mislead
SummaryThis paper presents Plumbline, a correctness validation system for training large language models, used in production at Amazon. It checks every stage of the training lifecycle, from training data to the model and whole runs, and also traces failures to their root causes. Across 3 frameworks and 5 models, it found 61 problems, 32 of them silent, grouped into seven failure patterns.
In briefAdjointFlow: Faster Physics-Based Solving of Scientific Inverse Problems
SummaryThis paper proposes AdjointFlow, a framework built on stochastic interpolants that injects known physics into the generative transport process. It uses a data-dependent, adjoint-based initialization to shorten the transport distance to the target and modifies the transport velocity so it stays regular when endpoint distributions concentrate. Experiments on inverse scattering, seismic imaging, and black hole imaging report more accurate reconstructions than conditional diffusion models and much faster inference than plug-and-play methods.
In briefOnline-adaptive fuzzy cognitive maps for neuro-symbolic financial forecasting
SummaryThis paper presents a neuro-symbolic framework combining online-adaptive Fuzzy Cognitive Maps (FCMs) with LSTM networks for financial forecasting. Framework I builds an FCM from live price series and macroeconomic indicators, adapting its edges online via an error-driven Hebbian rule and fusing its predictions with the LSTM's. Framework II builds a literature-grounded, LLM-augmented FCM in which each edge carries a verifiable citation. On NASDAQ Composite data, Framework I matched the LSTM baseline on state-directional accuracy and exceeded it by 7.6 percentage points on return-sign accuracy.
In briefHow Hyperbolic Are Hyperbolic Vision-Language Models?
SummaryThis paper examines whether the exponential map, which introduces hyperbolic curvature, meaningfully affects hyperbolic vision-language models. The authors find that across MERU, HyCoCLIP, ARGENT, and PHyCLIP the map is approximated well by the identity, and removing it at inference leaves scores unchanged. Euclidean models trained on CC3M under the same hierarchy losses match the hyperbolic counterparts in statistics, evaluation scores, and radial distributions.
WhyThe authors question hyperbolic VLMs by analyzing the exponential map and report matching results with Euclidean models trained on CC3M.
In briefClosing the burn-in gap: tight risk bounds for averaged SGD in linear regression
SummaryThis paper studies the excess risk of constant-stepsize online stochastic gradient descent with tail-averaging for high-dimensional linear regression. Building on Zou et al. (2021), it gives matching upper and lower bounds on total excess risk under a trajectory spectral feedback condition, and shows that both existing bounds are loose. The authors prove the condition holds in two constant-stepsize regimes and provide one- and two-dimensional constructions showing an exponential-versus-polynomial gap in the earlier lower bound.
WhyThe authors give matching tail-averaged SGD bounds closing a gap in Zou et al. (2021), plus one- and two-dimensional separation constructions.
In briefTraining-free discrete diffusion for multi-robot task allocation optimization
SummaryThis paper proposes Discrete Model-Based Diffusion (Discrete MBD), a training-free method for optimizing discrete variables in combinatorial problems such as multi-robot task allocation. It approximates categorical reverse transition distributions using Monte Carlo sampling. The authors derive a finite-step lower bound on finding a global minimizer, which approaches one as denoising steps increase. Experiments report that it outperforms sampling-based baselines in nearly all settings while approaching the solution quality of mixed-integer programming and reinforcement learning at much lower computational cost.
Haejoon Lee, Thomas Marshall Vielmetti, Xinyi Wang et al.
In briefLooping Deeper Without Growing the KV Cache for Encoder-Decoders
SummaryThis paper proposes a looped causal encoder-decoder (Looped CED) that adds recurrent depth without adding parameters or growing the key-value cache with each loop round. The authors place the recurrence in the encoder to iteratively refine global KV representations, and training with stochastic recurrence depth lets each round share the previous round's KV cache. On the 552B-parameter DeepSeek-V4.1-Flash, Looped CED holds 36.8x less KV per token and serves 3.6x higher throughput on 8K-token prompts.
In briefEnforcing Fairness Axioms in Loss-Based AI Alignment
SummaryThis paper studies whether reward models trained on pairwise comparisons, as in RLHF, respect Pareto optimality (PO), treating each principle as a voter. It shows PO can still fail for polynomial reward models and one-hidden-layer ReLU MLPs. Under idealized conditions with uniform comparison directions and equal comparison lengths, PO can be recovered for linear rewards, motivating dataset design as a remedy.
WhyThe authors model constitutional principles as voters and show loss-trained polynomial and one-hidden-layer MLP rewards can violate Pareto optimality, recovering it in an idealized linear setting.
In briefProfessors' views on AI-enabled cheating, mapped through forum analysis
SummaryThis study analyzes 2.01 million posts and comments from the r/Professors subreddit discussing cheating after the release of ChatGPT. It aims to understand why university professors want to discourage cheating and the strategies they use to address it. The authors synthesize these motivations and methods to identify caveats about integrating AI with academic integrity.
In briefA Technique to Model Product Information and User Reviews (MPIUR): a graph-based recommender combining text, images and reviews
SummaryThe paper proposes MPIUR, a multimodal graph-based recommendation model that encodes user reviews in heterogeneous graphs, aligns image and text representations, and adapts collaborative-semantic alignment to item interaction density. On the Amazon Reviews 2023 Musical Instruments and Baby categories, it achieves the best global nDCG@10 and Recall@10 among the evaluated methods. It also performs best on sparse items, suggesting that combining reviews with multimodal item content helps when interaction histories are limited.
In briefReconstructing Wind Profiles in the Lower Atmosphere Using Machine Learning on Radiosonde Data
SummaryThis paper compares modern machine learning models with traditional analytical formulas for reconstructing wind profiles in the atmospheric boundary layer, using 158,097 IGRA radiosonde soundings. Three tabular learners (CatBoost, TabM, and FT-Transformer) achieve errors of 1.3 to 1.6 m/s, compared with about 3 m/s for power-law and Monin-Obukhov fits, and their differences are under 0.05 m/s once predictors are fixed. The authors identify an empirical error floor of about 1.77 m/s for point-wise predictions using standard radiosonde features. A Fourier Neural Operator used as a post-hoc corrector reduces near-surface error at U.S. stations but leaves upper-layer error unchanged.
In briefWhy Students Cheat with AI: Instructors' Views from Forum Analysis
SummaryThis paper analyzes 2.01 million posts on the Reddit forum r/Professors to examine how instructors perceive students' reasons for using AI in prohibited ways. Qualitative analysis identifies four main themes: academic system gaming, a digital-academic literacy gap, performance anxiety, and normalization of dishonesty.
Iman Mohammadi, Sina Rismanchian, Ali Keramati et al.
In briefWhen Predictions Are Enough: A Protocol for Combining Frozen Experts
SummaryThe paper proposes a reusable evaluation protocol for deciding whether combining features from frozen models beats combining their final predictions, and applies it to 21 frozen deepfake detectors from DF40. It also examines how evaluation choices such as baseline strength and train/test overlap change the conclusions. Under leave-one-generator-out evaluation, logistic regression on features from six architecture-diverse experts improves macro AUROC by 0.081 over the best prediction combiner in each fold.
Alessandro Pereira, Keith J Ransom, Lewis Mitchell
In briefDEPUTYBENCH: A benchmark for testing contextual safety of enterprise AI agents
SummaryThis paper introduces DEPUTYBENCH, an executable benchmark of 79 base items and 375 instances with paired permission contexts. Across 12 models, revised audits report 88.0% pooled safety and 79.1% paired success. Agents more often restricted execution when evidence was missing (10.3%) than when permission was explicitly denied (3.5%), mostly in four privacy tasks requiring complete counts or person-specific consent.
In briefMulti-Stage Phase-Aware Self-Supervised MRI Reconstruction for Multi-Echo Gradient-Echo Imaging
SummaryThis paper presents MSPR-GRE, a self-supervised method for accelerating high-resolution multi-echo gradient-echo MRI while preserving anatomical detail and complex echo information. It combines progressive k-space expansion with phase estimation and refinement, and trains without fully sampled target images. Evaluated on 9.4 T ex vivo mouse-brain data and retrospectively undersampled 7 T human-brain data, it generally improved image, T2* map, and QSM metrics at acceleration factors of 4, 8, and 12. The authors note that generalizability and phase-estimation accuracy require further validation.
In briefProTEUS: a mutation-aware world model for closed-loop protein evolution
SummaryThe paper presents ProTEUS, a world model for closed-loop protein evolution that predicts the functional effects of mutations. Pretrained on deep mutational scanning data, it uses the MutationFormer architecture to learn a joint representation of sequence, evolutionary, and structural information. The authors also developed a physics-constrained all-atom predictor reported to be over 10 times faster than AlphaFold3. In two closed-loop engineering campaigns on aldehyde dehydrogenase and levansucrase, the resulting variants reached more than 10 times the activity of wild type.
In briefMass-conserving deep learning for distributed hydrologic modeling, tested in a snow-dominated watershed
SummaryThis paper introduces PECAN, a mass-conserving convolutional LSTM framework for distributed hydrologic modeling, available in two variants with strict and relaxed conservation constraints. It is trained only on gauge streamflow and tested in a snow-dominated Colorado watershed against lumped deep learning models and the VIC process-based model. PECAN gives competitive outlet streamflow accuracy and spatially explicit latent states that resemble soil moisture dynamics. However, it prematurely removes winter snow storage instead of retaining it for spring release, exposing a structural limitation.
Yihan Wang, Lujun Zhang, N. Benjamin Erichson et al.
In briefMeasuring How Definitions Shift LLM Annotations of Social Science Concepts
SummaryThis paper examines how large language models label social science concepts such as hate speech or fake news when given different definitions. The authors have the model annotate the same texts under two competing definitions and measure the disagreement, using a paraphrased definition as a control. Across six concepts and three LLMs, swapping competing definitions changes more annotations on average than paraphrasing does. The authors conclude that annotations depend on the definition given, so they should be reported together with it.
Bogdan Mamaev, Qizhou Wang, Christopher Leckie et al.
In briefVC-Dimension and Generalization in Geometric Machine Learning for Active Learning
SummaryThis paper computes the VC-dimension of metric ball classifiers in Riemannian model spaces (Euclidean, spherical, hyperbolic) and gives upper and lower bounds for other settings, including normed spaces, flat tori, and general Riemannian manifolds. Where the VC-dimension is large or infinite, the authors prove generalization bounds under extra assumptions on the data distribution using covering arguments. The results are applied to coverage-based algorithms in active learning.
Gokul Bhusal, Delanna Do, Ekaterina Merkurjev et al.
In briefSeparating Spatial Position Effects in Multimodal Rotary Embeddings for Vision-Language Models
SummaryVision-language models often use Multimodal Rotary Position Embedding (MRoPE), which also assigns height and width coordinates to text tokens, introducing spatial position effects into text-vision attention. This paper separates intra-visual spatial structure from these cross-modal effects and uses controlled experiments to isolate their contributions. The results show that cross-modal spatial effects offer little benefit as spatial references and can even hurt performance, while intra-visual spatial encoding remains useful for fine-grained grounding.
In briefDo AI Agents Need Preventive Care? A Cohort Design for Studying Agents
SummaryThis paper proposes an epidemiologic cohort design for studying agentic AI, borrowing principles from human population studies. Individual agents are treated as cohort members, their underlying model as genetic makeup, accumulated work as the time scale, and naturally occurring behaviors as exposures that may affect later outcomes. The authors illustrate the framework with two prospective observational cohorts, one examining curiosity and probing as a safety question and another examining data checking and analytical accuracy as a reliability question.
In briefStochastic projections and rank-aware learning rates for federated LoRA, a method for fine-tuning models across distributed devices
SummaryThis paper addresses fine-tuning large foundation models across resource-constrained distributed clients. It proposes SPRA-LoRA, which freezes and periodically resamples the A matrices on each client and scales learning rates by each client's adaptation rank. The authors report that this halves client overhead, prove convergence with heterogeneous client resources, and show consistent gains over state-of-the-art baselines on benchmark datasets.
Saber Malekmohammadi, Kevin Kuo, Virginia Smith et al.
In briefUni-Loc: Weakly Supervised 3D CT Lesion Localization via Spatially Grounded Vision-Language Classification
SummaryThis paper proposes Uni-Loc, a vision-language framework that performs both pathology classification and 3D localization in CT scans using only scan-level supervision. Predictions and localization maps are derived from spatial similarities between pathology-conditioned queries and image features, with gradient-guided thresholding identifying pathology regions. On five localization tasks with 355 annotated CT scans, it achieves macro-averaged voxel-level AUROC/AUPR of 96.1%/34.5%, outperforming comparison methods.
In briefAgent-guided discovery of exchange-correlation functionals, checked with physics-based validation
SummaryThe paper connects frontier LLM agents to a GPU evaluator via the Model Context Protocol to search for new forms of exchange-correlation functionals in density functional theory. The resulting 13-parameter functional, O159, multiplies same-spin correlation by a smooth density window. It lowers the held-out MGCDB84 test error of ωB97M-V by 11.7% after self-consistent evaluation.
In briefF3-AGI v4.0: A CPU-run reasoning system using energy-gated active inference and program synthesis for few-shot puzzles
SummaryThis paper presents F3-AGI v4.0, a CPU-native neuro-symbolic and active inference architecture for few-shot abstract reasoning on ARC-AGI. It combines metabolic plasticity optimization, adaptive complexity-guided program synthesis with a residual patch synthesizer, and exploration driven by Expected Free Energy. On the 400-task ARC-AGI-1 public evaluation set, run offline without LLM calls, it solves 57 tasks (14.25% pass@2) at a mean latency of 1.62 seconds per task.
In briefRobust Flow Matching Against Corrupted Targets and Noisy Labels
SummaryThis paper studies how to make Flow Matching generative models robust when training data is corrupted. The authors analyze how target corruption and label noise each affect the training objective. They propose residual-based sample weighting methods: a Temporal Residual Score for target corruption and a Comparative Conditional Score for label noise. Experiments on controlled corruption and real-world noisy labels show gains over standard Flow Matching and robust-training baselines.
In briefMicroLens: Reading, Tracing, and Editing a Language Model's Hidden States with Two Lenses
SummaryThis paper presents an interactive viewer that reads, traces, and edits the residual stream of a language model on a layers-by-tokens grid. Each cell can be decoded with the logit lens or the Jacobian lens (J-lens), traced back to its source tokens, and edited with the model rerun on the spot. The authors demonstrate three usage scenarios on Gemma-3-4B-it, backed by sweeps over a few hundred prompts.
In briefA probabilistic model of visual segmentation that explains early visual cortex dynamics
SummaryThe paper proposes a biologically plausible model of cortical computation in which inferring visual features is coupled with inferring how those features group into segments. The model iterates between these two inferences through recurrence and feedback, producing dynamics in the neural representation of features. The authors report that it predicts gain and variability in responses to natural images and captures classical single-neuron findings such as gain dynamics and variability decay.
In briefNetwork-based HIV intervention planning using R2C-Agent, which infers hidden contact networks from peer referral data
SummaryThis paper addresses how to plan network-based HIV interventions when only peer referral networks, not the hidden contact networks that drive transmission, can be observed. The authors propose Referral-to-Contact Diffusion (R2C-Diff), a conditional graph diffusion model that treats the referral process as corruption of the contact network and learns to reverse it. The R2C-Agent plans interventions over contact networks sampled from this model, and simulated experiments on real-world HIV network data show it outperforms baselines from interdisciplinary literature.
Akseli Kangaslahti, Milind Tambe, Alastair van Heerden et al.
In briefOne Trajectory, Two Requirements: Accuracy and Local Stability in Euler Sampling for Flow Matching Policies
SummaryThe paper examines how many Euler steps flow matching robot policies should use at inference time. It argues that a step count accurate along the nominal noise-to-action trajectory can still be too coarse for strongly contracting directions nearby. Experiments on an exact two-coordinate system, controlled modifications of six trained flow models, and a frozen π0.5-LIBERO checkpoint show that endpoint accuracy alone cannot rule out a separate stability requirement.
In briefABRR: A post-processing method that refines video moment localization from text queries using Wasserstein distance and precision-based re-ranking.
SummaryThis paper proposes ABRR, a framework for temporal sentence grounding, which locates the video segments matching a natural language query in untrimmed videos. ABRR is a model-agnostic post-processing approach that improves existing models without retraining, using temporal distribution smoothing, optimal transport-based boundary refinement, and quality-driven re-ranking. Experiments on Charades-STA, ActivityNet Captions, and QVHighlights show consistent gains at stringent IoU thresholds and improved ranking performance.
In briefPegasusAI Studio: Making Shared Research Computing Understandable and Accountable for AI Agents
SummaryThis paper presents PegasusAI Studio, an AI-assisted science gateway built on the Pegasus and HTCondor stack. It lets AI coding agents understand shared cyberinfrastructure, attributes each agent action to a user and limits it to that user's privileges, and adds approval controls for state-changing actions. The authors evaluate it through case studies including CASA weather workflow modernization, gravitational-wave reanalysis, EEG analysis, and EHT M87 image reconstruction, concluding that these mechanisms operate as designed.
Komal Thareja, Mats Rynge, Berent Aldikacti et al.
In briefAdapting Public Elbow X-ray Fracture Models to Local Data via Domain-Adversarial Learning
SummaryThis paper studies domain shift in classifying adult elbow fractures on radiographs when a model is trained on the large public MURA dataset and deployed on a small institutional dataset (UofL). The authors use a domain-adversarial neural network (DANN) with a gradient-reversal domain head to learn domain-invariant features while predicting fracture presence. On UofL test data, measured by AUC-ROC and accuracy, DANN outperformed source-only and naive merged-training baselines.
In briefTrain High, Ship Low: Efficient LoRA Synchronization for Distributed On-Policy Post-Training
SummaryThis paper addresses the bottleneck of sending LoRA adapters to rollout workers in distributed on-policy post-training over bandwidth-limited networks. The authors propose HiLo, which replaces parameter transmission with compressed gradient dissemination and local optimizer replay so that learners and workers keep consistent policy states. They also introduce Spar, a budget-aware gradient compressor combining spectral truncation, adaptive rank allocation, and error feedback to decouple training rank from communication cost. Experiments on on-policy distillation and reward-guided post-training show HiLo with Spar stays close to full synchronization quality while sending as little as 1.6% of adapter bytes, speeding end-to-end updates by up to 2.2×.
In briefDUO: Dual Readouts of Diffusion Models for Detecting Out-of-Distribution Inputs
SummaryThis paper proposes DUO, a method that detects inputs falling outside a diffusion model's training distribution. It combines reconstruction error with the leading sensitivity spectrum of intermediate denoising features, requiring no supervision or training. The authors report that the sensitivity signal stays informative when reconstruction error is misleading. DUO improves average AUROC by about 6 points over the strongest baselines, reduces inference cost, and achieves state-of-the-art results on near-OOD and high-resolution benchmarks.
In briefNeural network-assisted CLEAN for faster multipath parameter estimation in MIMO systems
SummaryThe paper proposes NN-CLEAN, which replaces the multidimensional grid search in each CLEAN extraction step with a multi-head classifier while keeping least-squares path gain estimation and residual cancellation. On synthetic data, it achieves about 27 times speedup and a 52-fold reduction in peak GPU memory versus grid-search CLEAN for three-path extraction. On Sionna ray-tracing channels, without retraining, it substantially reduces channel reconstruction MSE and BER compared with interpolated least-squares estimation, though grid-search CLEAN remains more accurate.
In briefAdapting a video foundation model to diagnose heart failure from echocardiography
SummaryThis paper fine-tunes TimeSformer, a pretrained video foundation model, to automatically diagnose reduced ejection fraction heart failure (HFrEF) from apical four-chamber echocardiography videos. The model is trained on the public RVENet A4C dataset using a phase-aware clip generation strategy that captures both systolic and diastolic dynamics. Full fine-tuning outperforms classifier-only tuning and a 3D CNN baseline.
Mahyar Bolhassani, Tijana Geroski, Narayana S. Singam et al.
In briefDetecting change points in time series with Gaussian processes
SummaryThis paper proposes a general Gaussian process framework for detecting change points in time series, defining a change as a shift in the hyperparameters of the Gaussian process prior. The problem is formulated as statistical hypothesis testing and evaluated for both offline and online detection of multiple change points. The method does not require a detailed specification of the time series function before and after a change, since hyperparameters are learned from data. Its performance is assessed on both synthetic and real datasets.
Olga Isupova, Danil Kuzin, Fredrik Gustafsson et al.
In briefDecode Token Parallelism (TKNP): memory-efficient distributed attention for scalable LLM decoding
SummaryThis paper addresses decoding for large language models serving high-concurrency, long-context workloads, where existing parallelization strategies each have limitations. It proposes Decode Token Parallelism (TKNP), a decode-pool architecture that separates weight placement from attention ownership, distributing one model copy across nodes in layer blocks while each node computes attention for its own requests against local KV cache. Implemented in vLLM and evaluated on Llama 3.1 405B and Nemotron-4 340B using up to 64 H100 GPUs.
In briefMRFoundry: Knowledge-guided agent loops for end-to-end MRI research
SummaryThis paper presents MRFoundry, an open-source, extensible package that links MRI domain knowledge to AI agent actions and output checks, and introduces MRI Agent Bench for comparison. Across 240 paired Core evaluations covering 24 task types and ten models, all-checks-passed completion rose from 67.5% without the package to 78.3% with it. On 134 Hard pairs, completion rose from 36.6% to 44.8%.
In briefRetinexGS: Retinex-Inspired Gaussian Splatting for Low-Light RAW Reconstruction
SummaryThis paper presents RetinexGS, a 3D Gaussian Splatting framework that works on RAW images to reconstruct scenes under low light for novel-view synthesis. It splits each Gaussian's color into a view-independent base color and a shared position- and view-dependent modulation field, and weights the loss using DNG noise metadata. Across five evaluated scenes, it improves mean sRGB PSNR over LE3D by 2.18 dB and over RawNeRF by 2.71 dB while supporting real-time rendering.
In briefA paper analyzing how pretraining, fine-tuning, and reinforcement learning interact and how compute should be split between them
SummaryThe paper introduces an analytically tractable high-dimensional model combining unsupervised pretraining, supervised fine-tuning with noisy labels, and reinforcement learning with sparse rewards. Using random matrix theory, it derives low-dimensional learning dynamics that closely match simulations. It finds that SFT initialization shortens the initial RL plateau, a result also observed in OLMo-2 1B on GSM8K. The authors also analyze how compute should be allocated across the three stages and how RL selectively strengthens task-relevant pretrained skills.
Nishil Patel, Jin Hwa Lee, Basile Confavreux et al.
In briefOff-policy learning using multi-step excursion policies
SummaryThis paper studies off-policy reinforcement learning, where a target policy's value is learned from data generated by a different behavior policy. The authors propose excursion policies, which follow the target policy for a multi-step excursion before reverting to the behavior policy, as an alternative to linear interpolation between the two. They derive dynamic programming results and develop provably sound off-policy temporal-difference algorithms for estimating excursion values. On continuous control benchmarks, excursion policies offer an efficiency-stability trade-off comparable to mixture policies.
Jiamin He, Mark Rowland, Zhaohan Daniel Guo et al.
In briefRaMOD: Detecting Moving Objects from Raw Radar Data While the Vehicle Is Moving
SummaryRaMOD is a real-time framework that detects moving objects and estimates their velocity directly from raw range-azimuth-Doppler cubes captured by a vehicle-mounted single-chip millimeter-wave radar. It estimates the ego motion frame by frame, uses it to predict and suppress Doppler returns from stationary structures, and then predicts labeled detection boxes from multi-scale temporal features. On the BRIDGE-Liv dataset, it outperforms all compared raw-radar methods, reaching 80.8% cube mAP at IoU 0.30 and a velocity MAE of 1.128 m/s.
In briefWhat drives symbolic mechanisms to emerge in large language models?
SummaryThe authors trained neural networks from scratch on abstract sequence tasks while varying architecture and data distribution, then analyzed the resulting symbolic mechanisms with representational, attentional, and causal mediation methods. Greater data diversity produced stronger symbolic signatures, and similar signatures appeared even in modified transformers and multilayer perceptrons. The study concludes that data diversity, rather than architecture, is the primary driver of emergent symbolic computation.
In briefAdaptive Residual Quantization for Compact Storage in Temporal Action Segmentation
SummaryThis paper introduces ARQ-VAE, a compact memory representation for temporal action segmentation that does not require frame-wise labels. It learns a discrete residual quantization of frame-level video features and adaptively truncates the residual chain per frame, keeping only the prefix that best reconstructs each frame, followed by a lightweight fine-tuning stage. On Breakfast, training-set storage drops from 28 GB to 10 MB while maintaining strong segmentation performance. The same stored memory also supports incremental and unsupervised temporal action segmentation.
Gerard Donahue, Guven Gergerli, Ayush Gupta et al.
In briefWhich Acoustic Features Hold Up After Compression? First-Formant Statistics for Codec-Robust Audio Deepfake Detection
SummaryThis paper examines why audio deepfake detectors that perform well on clean recordings often weaken after telephone or platform compression. The authors screened 36 acoustic features on ASVspoof 2019 and tested them on the ASVspoof 2021 LA set across seven codec conditions. Four first-formant (F1) statistics were most stable, and an RBF-SVM using only these four features degraded less under codec mismatch than the 36-feature SVM.
Yash Sukhdeve (1), Aaron Storey (2), Masudul H. Imtiaz (1) ((1) Department of Electrical et al.
In briefReliability of long-horizon LLM agents: a trajectory-based study of where errors enter and which controls catch them
SummaryThis paper studies why LLM agents fail during long tasks, proposing a record that traces each fault's origin, propagation, detection, response, and consequence, and names the control families that could cover it. The authors annotated 95 failed runs from six multi-agent systems in the public Who&When and MAD corpora using this protocol. They found faults usually enter early, and in 72% of failed runs the trajectory already contained evidence against the error, yet in 88% of those no agent acted on it.
In briefCurie's Jarvis: a robot performs chemistry experiments from printed instructions
SummaryThis paper presents Curie's Jarvis, a system that lets a robot arm carry out chemistry experiments on an ordinary bench designed for people. It reads the steps from a photo of a printed protocol sheet, finds containers without fixed positions or markers, and tells identical-looking containers apart by reading their labels across four camera views. On a Franka Panda arm, it completed 5 of 5 runs of one protocol and 4 of 5 runs of another with no human intervention.
Rumi Loghmani, Bozhang Song, Victor Alvarado et al.
In briefDomain-Routed Delta Optimization for Multi-Teacher On-Policy Distillation
SummaryThis paper proposes Domain-Routed Delta Optimization (DRD-MOPD) for multi-teacher on-policy distillation. The student combines a frozen base with trainable components initialized from expert-minus-base weights, and each domain's samples update specified components. The authors measure how well the original expert geometry is retained using angles, projection coefficients, and energy. Their checkpoint scores 32.72% macro-average across six benchmarks, compared with 32.44% for its initial task-arithmetic merge.
In briefExpertCrowd-SWE: A Benchmark for Testing AI Coding Agents on Expert-Written Tasks in Unfamiliar Codebases
SummaryExpertCrowd-SWE (EC-SWE) is a benchmark of 100 multi-file software engineering tasks written by practising engineers, designed to test autonomous coding agents on large, unfamiliar codebases. Grading uses weighted, mostly executable criteria that award partial credit, along with must-pass blockers that reflect what a maintainer would refuse to merge. The authors evaluated seven frontier models over 3,495 scored runs, finding that Claude Opus 5.5 (67.3%) and GPT-6 Astra (63.7%) lead without a statistically significant gap.
Josip Vrdoljak, Akash Singh, Bas Steunebrink et al.
In briefRethinking how video capsule endoscopy classifiers are evaluated
SummaryThis paper studies automated classification of video capsule endoscopy images, introducing DMSNet, an edge-guided attention network, and benchmarking it against 18 other architectures on Kvasir-Capsule. It shows that model rankings and scores change between random frame-level validation and source-video-disjoint validation. Performance also drops substantially on the external CV2024 dataset. The authors conclude that source-aware validation and rigorous external evaluation are needed for these systems.
In briefGigaEdge Spench: a toolkit for practical real-time speech enhancement
SummaryThis paper presents GigaEdge Spench, an open-source SDK for streaming speech enhancement that runs on CPU servers, mobile devices, and in browsers. Applications can combine neural denoising, normalization and dynamics blocks, sample-rate conversion, and echo cancellation where supported. The release includes an 8 kHz INT8 denoiser and 48 kHz FP32 discriminative and hybrid models. The authors evaluate these models on EARS-WHAM v2 and compare quality and processing cost with RNNoise and DPDFNet2.
Artem Sokolov, Artem Khaidarov, Yana Lysova et al.
In briefSTAR: Zero-shot method for tiered radio resource allocation based on semantic importance
SummaryThis paper addresses remote inference, where edge devices send high-resolution images over wireless links so an AI model can answer questions about them. It proposes STAR, a zero-shot, domain-agnostic framework that derives a per-pixel semantic-criticality indicator from a frozen vision-language model's attention and assigns task-critical pixels to a coded tier and the full image to an uncoded, downsampled base layer. Simulations on a 5G New Radio link show semantic accuracy of at least 0.98 for Es/N0 ≥ -4 dB and reductions of 59.5% and 91.5% in resource elements on two datasets. A deep joint source-channel coding baseline uses fewer resources but requires retraining with labeled data for each new task or domain.
Albert Shaju, Christo Kurisummoottil Thomas, Mingzhe Chen
In briefNCP: Verifier-Guided Optimization of Weight-Space Control Policies
SummaryThis paper presents the Neural Control Plane (NCP), a closed-loop system that observes language-model execution, generates candidate weight-space intervention rules, evaluates them, and keeps a versioned table of admitted policies. In a local SmolLM-135M-Instruct experiment, one projection reduced validation token repetition from 0.4133 to 0.2191 with a perplexity change of -0.0574%. However, none of the three candidates produced newly correct exact answers, so all were archived and the original active policy was retained.
In briefChoosing by Telling Apart: Differential Evidence for Medical Reasoning
SummaryThis paper addresses medical multiple-choice questions, where answer options often share overlapping evidence. It proposes a retrieval-grounded framework that extracts option-specific differential evidence and organizes it into concise prompts for answer selection. Experiments report consistent accuracy gains across several datasets and target LLMs, outperforming directly using retrieved chunks or general hints generated by closed-source LLMs.
In briefW-ISA: Transactional Execution of Weight-Space Intervention Programs
SummaryThis paper presents W-ISA, a software instruction set and transactional runtime for running weight-space intervention programs on pretrained language models. Shape-checked instructions express parameter updates, projections, ablations, and rank-one transformations, and the runtime restores saved parameters after completion or interruption. Experiments on SmolLM-135M-Instruct and Qwen2.5-1.5B-Instruct compare materialized rank-one updates with equivalent PyTorch hooks, which produced identical greedy sequences across 45 timed requests. On the WikiText-2 raw test split, a combined attention-head ablation and MLP projection raised perplexity from 22.2353 to 22.3188.
In briefFaster second-order training for physics-informed networks using sparse Gauss-Newton methods
SummaryThis paper presents an efficient Gauss-Newton optimization method for finite basis physics-informed neural networks (FBPINNs). It exploits the block sparsity created by the overlapping domain decomposition of FBPINNs, using sparse automatic differentiation and graph coloring to compress Jacobians. The resulting block-sparse Gauss-Newton system is solved with sparse direct or preconditioned iterative solvers. Experiments on multiscale PDEs show high accuracy and substantially faster optimization than first-order baselines, with good scalability to large problems.
In briefContinuous Horizon Forecasting: a multi-step method that treats the forecast horizon as a continuous query
SummaryThis paper introduces the Continuous Horizon (CH) strategy for multi-horizon forecasting, which treats forecasts as a continuous operator and the horizon as a query variable rather than a fixed index. The authors provide theoretical foundations, including a Universal Operator Approximation Theorem and a continuous-horizon bias-variance decomposition that argues CH reduces estimation variance compared with discrete strategies. Experiments on a simulation study and 16 large-scale M-series datasets show competitive RMSE accuracy, along with parallel inference, targeted horizon querying, and interpolation and extrapolation to unseen horizons.
Malcolm Wolff, Elliott Zackrone, Souhaib Ben Taieb et al.
In briefWhether deleting reasoning history hurts depends on the model: a three-condition test on seven open models
SummaryThe paper measures how deleting a reasoning model's earlier reasoning history affects task results in multi-turn tool calling. Using seven open-weight reasoning models, three seeds, 794 tasks and the official checker, it compares three rendering conditions in a blind design. The effect of the same deletion ranges from -4.4 to +51.3 points, with both ends excluding zero. The authors argue this deletion sensitivity is a stable, model-level property.
In briefMediPFN: amortized in-context learning for causal mediation confidence intervals
SummaryThis paper introduces MediPFN, a framework that adapts a pretrained tabular foundation model to predict nuisance functions in causal mediation analysis and combines them through a cross-fitted influence-function estimator. A separate head learns quantiles of the estimator's standardized error from simulated tables with known indirect effects, followed by supervised fine-tuning, symmetrization, and independent calibration to produce confidence intervals. Experiments show coverage closer to the nominal level than several comparators, and uncertainty quantification running 59.1 times faster than bootstrap in matched comparisons.
In briefHow shared parameters both suppress and amplify reward hacking in RLVR
SummaryThis paper studies reinforcement learning with verifiable rewards (RLVR) against an imperfect verifier in a controlled setting where the full response distribution can be computed exactly. The authors vary how universal a verifier leak is, meaning how many prompts accept the same wrong response, along with the verifier's false-positive rate. They find that when parameters are shared, a wrong answer accepted by most prompts is amplified while one accepted by few prompts is pushed below the base rate. The authors propose this mechanism as a hypothesis for why earlier studies disagree.
In briefReliability of Claude Code as an AI System for Grading Handwritten University Exams
SummaryThis study tests whether Claude Code, an agentic system built on the Claude Opus 4.6 language model, can reliably grade handwritten university exams. It graded 20-mark mid-term papers from 60 students in a Saudi computer graphics course and was compared against an expert instructor's grading, with consistency checked across two sessions a week apart. AI totals agreed strongly with human scores, and its feedback was largely accurate, but drawing items and boundary cases showed weaknesses. The authors conclude it can assist with objective and short-answer questions under safeguards but is not yet ready to replace instructor judgement.
In briefLatin American Studies Since the 1980s: Shift From Utopian Hopes to Assimilated Stability
SummaryThis paper examines how Latin American Studies has changed since 1982, drawing on three leading journals in the field. Using network analysis, text mining, and bibliometrics, the authors trace shifts in disciplinary positioning, authorship, and citation patterns. They argue the field has consolidated institutionally, with core journals central in disciplinary networks, but this has reinforced hierarchies favoring Anglophone norms and marginalised Latin America-based journals.
Thiago Dumont Oliveira, Juan Grigera, Pedro Mendes Loureiro
In briefCounting Under Disagreement: Measuring Dollar Totals with AI Labels and Limited Human Review
SummaryThis paper studies how AI tools can help measure monetary totals across many administrative records when human readers disagree on labels. The testbed uses 121,070 US tax-filing grant-purpose texts covering 799,675 records, labelled by a rule-based classifier, three language models, and three independent human readers. It proposes estimators and two rules for accepting machine labels at a stated dollar-weighted error, and finds that a two-phase human review policy lowers the total's RMSE by 28% at a budget of 100 texts.
WhyThe authors combine dollar-weighted measurement, selective machine labelling and budgeted human review, giving exact conditions and cross-fitting; they report simulations and replay on 121,070 tax-fil
In briefBefore Words, Beyond Speech: A Benchmark for Nonverbal Social Reasoning in Young Children
SummaryThis paper introduces NEST, a benchmark for evaluating nonverbal social reasoning in early childhood. It contains manually annotated clips of about 12 seconds, drawn from varied natural settings, cultural contexts, and developmental stages. The authors propose a layered evaluation scheme covering contextual, behavioral, interpersonal, and open-ended descriptions. Tests on current vision-language models show strong contextual recognition but clear gaps in decoding precise behavioral and interpersonal cues.
Marie Amale Huynh, Laura Bravo-S\'anchez, Lauren Klein et al.
In briefNear-optimal sample complexity for deterministic agnostic PAC learning
SummaryThis paper studies agnostic PAC learning when labels come from an arbitrary deterministic function that may not belong to the concept class. For classes with VC dimension d and finite diameter K, the authors show learnability with sample complexity roughly K^(1/3)d^(2/3)/ε, and construct classes where this bound is tight up to logarithmic factors. They also examine the online version, showing its minimax regret is partly controlled by the class diameter.
Shai Ben-David, Steve Hanneke, Farnam Mansouri et al.
In briefHiCAP: Personality-Based Hierarchical Cultural Adaptation for Dialogue Generation
SummaryThis paper proposes HiCAP, a framework that incorporates personality traits into culturally adaptive dialogue generation. It uses a personality-conditioned control mechanism that first aligns responses with cultural tone, then refines language and content according to individual personality attributes. Experiments on the CulturalPersonas and CPED datasets report that HiCAP achieves the strongest overall performance across various dialogue generation metrics.
