In briefSelf-training with contrast-frame subtraction for coronary X-ray vessel segmentation across hospitals
SummaryThis paper addresses the drop in performance of coronary X-ray angiography vessel segmentation models on data from centres not seen during training. The authors generate pseudo-labels from nnU-Net predictions and a contrast map obtained by subtracting a pre-contrast frame, then self-train without extra manual annotation. Self-training improved results on external datasets, and the size of the gain depended largely on the sensitivity of the model that produced the initial pseudo-labels.
In briefMAServeBench: Benchmark for Serving Multi-Agent LLM Systems with Hierarchical Collaboration Workloads
SummaryThis paper presents MAServeBench, a benchmark for evaluating serving systems that run multi-agent large language model applications. It treats how agents collaborate and exchange context as a configurable part of the workload, so the same tasks can produce different serving demands. The benchmark records realized operations and dependencies for reproducible comparisons and links them to hardware behavior, showing why higher serving efficiency does not always mean tasks finish sooner.
Shengxuan Qiu, Haochen Huang, Shuzhang Zhong et al.
In briefDolphinRouting: Cluster-Level Routing for Split LLM Serving
SummaryThis paper presents DolphinRouting, a method for routing requests in large language model serving systems that separate prefill and decode phases. Requests are grouped into clusters by prompt length, cache-adjusted input lengths, and output-length information, and each cluster learns a binary choice between local and remote prefill from end-to-end SLO feedback. Evaluated on a real disaggregated cluster with public benchmarks, a mixed workload, and real data, the k-means variant reduces P90 TTFT by 5.7 to 7.9% and P90 TPOT by 2.6 to 10.1% compared with Dynamo.
In briefOnline knapsack with removable items using recourse and size bounds
SummaryThis paper studies the online unweighted knapsack problem, where items arrive one by one and must be accepted or rejected immediately to maximize the total size of accepted items. It allows previously accepted items to be removed and considers two extensions: knowing an upper bound on item sizes and being able to reinsert previously rejected items. The authors establish close upper and lower bounds on the competitive ratio under these combined assumptions. They also introduce adaptive online algorithms that do not require knowing the size upper bound yet achieve comparable performance guarantees.
Dimitris Fotakis, Laurent Gourv\`es, Aris Pagourtzis et al.
In briefOnly Accepted Tokens Count: Energy Accounting of Speculative Decoding Under Latency Targets
SummaryThis paper examines the energy cost of speculative decoding, in which a drafter proposes several future tokens that a target model verifies in one forward pass. The energy saving depends on how many drafted tokens are accepted, which varies by model, drafting scheme, and text. Measured on H100 and H200 GPUs with SGLang at batch sizes up to 64 across 5 corpora, the saving shrinks as batch size grows and can become a loss at large batches. Speculation only adds energy savings when acceptance is high.
In briefLoRe-MIM: A self-supervised method for learning features from whole-body PET/CT scans using masked image reconstruction
SummaryThis paper addresses the shortage of expert annotations for whole-body PET/CT deep learning by assembling a multi-tracer, multi-institutional dataset of 6,531 scans. It proposes LoRe-MIM, a self-supervised framework that learns multimodal representations by reconstructing masked images, where masked regions are either zeroed out (Lost) or replaced with the corresponding complementary imaging modality (Replaced). The learned representations are evaluated on tasks including multimodal reconstruction, cancer versus non-cancer patch classification, whole-body lesion segmentation, label-efficient training, and robustness assessment. The authors report that LoRe-MIM consistently outperforms corresponding state-of-the-art baselines across these tasks.
Amartya Bhattacharya, Bashirul Azam Biswas, Biratal Raj Wagle et al.
In briefGuiding latent reasoning exploration with potential-based trajectory balance
SummaryThis paper introduces SubLAT, a method for continuous latent reasoning in language models that combines a relative answer potential with Subtrajectory Balance to learn stochastic latent exploration. The model is trained jointly with answer supervision, CoT supervision, and representation distillation. Experiments on arithmetic and deductive reasoning show that solution coverage increases with larger sampling budgets and improves on several structural out-of-distribution benchmarks.
In briefSelf-supervised brain encoders that align across people in fMRI
SummaryThe paper adapts a joint embedding predictive architecture (JEPA) to learn participant-specific fMRI encoders from brain data alone, without ever seeing stimulus images. On the Natural Scenes Dataset (NSD), the embeddings achieve competitive results on brain captioning and image reconstruction against supervised pipelines that use images and language. The authors also show that encoders for different participants can be aligned fully unsupervised, so a decoding head trained on one participant transfers to another without fine-tuning while keeping most of its within-subject performance.
Serwan Jassim, Victoria Bosch, Daniel Anthes et al.
In briefNeighborhood Elites: Quality-Diversity Search for Detecting Wrong Answers in QA
SummaryThis paper addresses the difficulty of detecting wrong answers from large language models in question answering. It proposes neighborhood elites (NE), a method that uses quality-diversity search to build answer-preserving input rewrites spread across two axes: the kind of edit and how explicitly the context supports the answer. Across five extractive-QA datasets and five instruction-tuned LLMs, NE improves failure detection over other neighborhood constructors in at least 24 of 25 pairs, and also outperforms semantic entropy computed over decoder samples.
Mesut Ceylan, Arda Baris Basaran, Elgar Fleisch et al.
In briefTaskFrame: A Framework Linking Task Planning to Frame-Level Control for Game Agents
SummaryThis paper presents TaskFrame, a hierarchical framework for game agents that connects task-level planning with frame-level keyboard and mouse control. A vision-language planner breaks a high-level goal into ordered atomic instructions, while a causal Transformer controller predicts actions at each game step using the active goal and recent observations. Experiments in Minecraft show competitive instruction decomposition compared with general-purpose vision-language models, and a higher combat success rate than the evaluated baselines in closed-loop evaluation.
In briefFrom MS/MS Evidence to Adaptive Spectroscopy: Reinforcement Learning Guided by Candidate Structures
SummaryThis paper proposes a structure-informed deep Q-network (SI-DQN) that uses existing MS/MS evidence to decide whether further characterization is needed, which measurement to request, and when to stop. The policy was evaluated on a Multimodal Spectroscopic Dataset containing simulated MS/MS, infrared, and NMR spectra. Results show higher Top-1 accuracy with far fewer additional measurements than complete acquisition, and lower composite loss than a rule-based policy.
In briefLearned terminal costs enable short-horizon MPC for autonomous trucks in busy highway traffic
SummaryThis paper addresses autonomous highway driving for heavy-duty trucks, combining Model Predictive Control (MPC) with Reinforcement Learning (RL). A finite-horizon MPC solved by dynamic programming uses a pre-trained RL model to estimate terminal cost. The method is tested in simulation with reactive surrounding traffic against pure MPC with zero terminal cost, rollout with a base-policy terminal cost, and pure RL. The hybrid approach achieves zero collisions and the lowest cost among all configurations.
Hooman Hamze, Benjamin Houshmand, Deepthi Pathare et al.
In briefWhen Requirements Interact: Long-Horizon Coding via Interdependent Specs
SummaryThis paper introduces CodeSpecWeave, a framework that synthesizes coding tasks with rich, interdependent specifications grounded in real developer seeds. A synthesis agent creates coupled requirements and executable tests, while a review agent audits solution trajectories from multiple models to ensure test reliability. The authors built 4,670 training tasks across 2,865 repositories and used reinforcement learning on them, substantially outperforming the base model and existing baselines on DeepSWE-v1.1. They also released CodeSpecWeave-Bench, a benchmark of 100 tasks and 1,044 specifications.
In briefStatistical Analysis of Verified Synthetic Data in Linear Regression
SummaryThis paper studies self-training on synthetic data within a linear regression framework. A pre-trained estimator generates data with injected noise, a verifier filters samples by a threshold, and the model is then fine-tuned on accepted samples. The authors derive finite-sample risk bounds across four regimes to show how verification threshold, generation noise, and pre-training error affect prediction risk.
In briefEstimating ground speed from Doppler spectra with per-frame confidence
SummaryThis paper proposes a method for estimating speed over ground from ground-facing Doppler spectra, aimed at mobile platforms such as railway systems. It builds on an autoencoder reconstruction approach, adding a speed estimation head on the latent features and a lightweight confidence head used at inference, with noise augmentation training. Experiments show improved speed estimation accuracy over classical baselines and confidence estimates that indicate the reliability of individual predictions under adverse signal conditions.
In briefConformal annotation with randomized expert review for online cell-type labeling
SummaryThis paper proposes CARED, a distribution-free framework for AI-assisted cell-type annotation under distribution shift. It randomly selects cases for expert review and uses the resulting labels to calibrate later decisions. The authors prove long-run control of cumulative error without stationarity or exchangeability assumptions. Experiments on pancreas, lung, and ovary annotation streams show that up to 70% of cells can bypass expert review while errors remain controlled.
In briefDistribution-calibrated, budget-neutral token credit for multimodal RL with verifiable rewards
SummaryThe paper proposes TACE, a two-stage estimator for advantage computation in GRPO. The first stage maps rewards to a standard normal via a one-dimensional optimal-transport map to align reward distributions across tasks and cap outliers. The second stage rank-calibrates token-level evidence weights within each trajectory and renormalizes them to mean one, conserving total credit and sign. On a multimodal reasoning suite, TACE achieves the best results on most benchmarks but not in every setting.
In briefReaction precedents let retrosynthesis prediction go beyond its training data
SummaryThis paper examines how far retrieved reaction precedents can make up for gaps in a retrosynthesis model's task-specific training coverage. Using 2.71 million reactions, the authors limited training and retrieval by transformation type or time, then expanded only the retrieval corpus while keeping model parameters fixed. Precedents substantially raised accuracy on transformations excluded from training and improved all 14 general-purpose models, with the best surpassing eight specialized models.
In briefLLM-guided planar loco-manipulation for quadruped robots using language planning and pushing policies
SummaryThe paper addresses robotic navigation in cluttered environments where reaching a goal may require physically moving obstacles. A language-conditioned task planner, fine-tuned with LoRA on synthetic data, generates variable-length sequences of parameterized actions executed by low-level reinforcement-learning policies. The planner outperforms a classical planner and a prompted frontier model on interactive navigation tasks. An end-to-end whole-body planar pushing policy reaches a 93.3% success rate in simulation, compared with 83.5% for a hierarchical baseline.
Georges Jetti, Michael Khayyat, Jean-Pierre Sleiman et al.
In briefEnvOS: Auditing AI computer agents in software that changes mid-task
SummaryThis paper introduces EnvOS, a framework for building and certifying environments whose software changes while an AI agent is working on a task. It checks before a run that changes fire correctly and tasks remain solvable, and re-checks verdicts from archived evidence afterward. Tested on stale-information scenarios in clinic booking and insurance-claim approval, the results show that whether an agent notices a change depends on the model and the interface shown to it.
In briefJudged in Company: Batching Items in One Prompt Makes LLM Verdicts Depend on Neighbours
SummaryThis paper examines whether asking a language model to grade many independent items in one prompt, a common cost-saving practice, changes the verdicts. Across three judgement tasks and five open-weight models, placing the same 2,000 items into different random batches changed 11-22% of decisions on harm and grading. Most of this variation comes from the order and position of neighbouring items, and majority voting over permutations does not remove it. The authors recommend grading ambiguous items one per prompt and reporting agreement with single-item grading when batching.
WhyThe authors show batched grading makes verdicts depend on co-batched items, measuring flip rates across three tasks and nine models and decomposing them.
In briefScienceJudge: Pattern-Based Data Augmentation for Checking Sentence-Level Scientific Claims
SummaryThis paper addresses checking whether each sentence in an AI-generated scientific paragraph is supported by a given multimodal evidence bundle. The authors propose Pattern Perturbation Augmentation (PPA), which models reporting errors as faithfulness-breaking transformations to create hard negative examples. Their ScienceJudge system combines vision-language and LLM predictors, raising the local development-set average score from 0.8124 to 0.8692. It ranked first on the official NLPCC 2026 Task 10 Track 1 leaderboard with a final score of 0.6890.
In briefMining translation pairs from linguistic archives for low-resource languages
SummaryThis paper proposes extracting parallel translation examples from linguistic archival materials such as grammars and dictionaries, which cover languages with little online presence. The authors build a pipeline combining an OCR model and a vision language model so that extracted text stays aligned with visual layout information. The extracted bitexts are used across 20 languages to train machine translation models and to support in-context learning for large language models. For in-context learning, the reported chrF2++ gains over zero-shot translation are +15.78 for low-resource and +6.94 for high-resource target languages.
Chutong Meng, Alec J. Bateman, Antonios Anastasopoulos
In briefEpiAgent: Epistemic Self-Play Framework for LLM Agents
SummaryThis paper proposes EpiAgent, a framework that gives LLM-based agents a learnable layer for maintaining beliefs about others' private information. The policy is split into an Epistemic Update stage and an Action Planning stage, optimized alternately through a bi-level formulation called Epistemic Self-Play, using an intrinsic prediction reward. The authors report that EpiAgent outperforms prompting-based and RL-based baselines in mixed and competitive scenarios.
