Search: “evaluation”

What Do Verifiable Rewards Teach Video-Language Models About Time? A Controlled Multi-Model Study

model-releasesarxiv-cs-cv

arXiv:2610.03792v1 Announce Type: new Abstract: Reinforcement learning from verifiable rewards (RLVR) has produced large reasoning gains in language models, and verifiable video benchmarks make it app

6 Oct 2026

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What Does an Observability Foundation Model Know?

DGX agent

arXiv:2610.05577v1 Announce Type: new Abstract: A linear probe can show that a label is recoverable from a model's hidden states, but not whether that goes beyond what the input already reveals, or wh

model-releasesarxiv-cs-lg

6 Oct 2026

WILLIE: A Unified Framework and Benchmark for Wound Classification, Segmentation, and Localization

model-releasesarxiv-cs-cv

arXiv:2610.05341v1 Announce Type: new Abstract: Chronic wound management affects over 8.2 million patients in the United States and imposes substantial clinical and economic burden. Clinical wound ass

6 Oct 2026

A generative-informed neuro-symbolic framework for syntactic ambiguity resolution: Evidence from Arabic DPs

arXiv:2610.02529v1 Announce Type: new Abstract: Syntactic ambiguity poses a persistent challenge for Arabic NLP, particularly in morphologically rich nominal constructions where multiple structu6ral i

Adaptive Mutual Distillation for Balanced Multi-Task Post-Training of Large Language Models

model-releasesarxiv-cs-cl

arXiv:2610.02856v1 Announce Type: new Abstract: Multi-task post-training of large language models (LLMs) aims to improve performance across tasks with unequal amounts of training data. Existing method

Adaptive Second-Order Solvers for Fast Stochastic Diffusion Sampling

arXiv:2610.03034v1 Announce Type: cross Abstract: Diffusion models rely on numerical solvers requiring time-discretization, which has a large influence on the tradeoff between sampling cost and qualit

ANCRe: Adaptive Neural Connection Reassignment for Efficient Depth Scaling

arXiv:2602.09009v2 Announce Type: replace-cross Abstract: Scaling network depth has been a central driver behind the success of modern foundation models, yet recent investigations suggest that deep la

Answering clinicians' questions over trial evidence tables with verifiable, feedback-driven language models

arXiv:2610.02576v1 Announce Type: new Abstract: Systematic reviews condense clinical trials into evidence tables, yet clinicians can interrogate these tables only through database queries, and many qu

Asterism: Exploring and Synthesizing Scattered Observations into Literature-Grounded Hypotheses and Theories

arXiv:2610.02673v1 Announce Type: cross Abstract: A theory draws many independent observations into one framework with novel hypotheses. A researcher building such a theory must synthesize observation

Author Representation Strategies for Zero-Shot Authorship Attribution: A Comparative Study of LLM-Based and Embedding-Based Approaches

arXiv:2610.03531v1 Announce Type: new Abstract: Authorship Attribution (AA) requires capturing fine-grained stylistic characteristics, making it particularly challenging in zero-shot (ZS) settings whe

Automating the Application of HCI Principles: Skills for On-Demand UI Construction, the Human-AI Space to Think, and the Future of HCI

arXiv:2610.02369v1 Announce Type: cross Abstract: Human-computer interaction (HCI) is in the middle of a transition: large language models can now generate functional user interfaces (UIs) on demand f

Beyond Reward Hacking: Proxy Divergence Across Four Layers of a Staged Humanoid Learning Pipeline

arXiv:2610.03196v1 Announce Type: new Abstract: A reinforcement-learning (RL) pipeline for a legged robot is assembled from proxies. A reward stands in for intended behaviour, a curriculum gate stands

safetyarxiv-cs-ro

Characterizing the Performance Gap in Human Activity Recognition for Older Adults

arXiv:2610.02711v1 Announce Type: cross Abstract: Human activity recognition (HAR) from wrist-worn accelerometers is increasingly used for health and behavioral tracking. Yet, most wearable HAR models

Contextual Flow Matching: Adaptive Step Selection in Flow Models for Efficient Visual Generation

arXiv:2610.03202v1 Announce Type: cross Abstract: Flow Matching enables high-quality visual generation via continuous-time dynamics, but inference remains costly due to multiple sequential function ev

Controlling Polar Exposure to Delay Memorization in Diffusion Models

arXiv:2610.02780v1 Announce Type: new Abstract: Diffusion models can reach useful sample quality before copying training examples, but fast optimization can compress this generalization window by acce

CORE: COverage CAlibration and Evicted-Mass REdistribution for KV Cache

arXiv:2610.02235v1 Announce Type: cross Abstract: Long-context decoding is increasingly constrained by key--value (KV) cache memory and bandwidth. Existing fixed-budget compression methods typically s

Local Ai

CreateScore: Domain-Theory-Informed Bayesian Routing for LLM-Based CV Screening

arXiv:2610.02972v1 Announce Type: new Abstract: Large language models (LLMs) can support rubric-based screening of CVs, but applying a high-capability model to every candidate and criterion is costly.

local-aiarxiv-cs-ai

D2K-Bench: Can LLM Agents Turn Expert Designs into Efficient GPU Kernels?

arXiv:2610.03226v1 Announce Type: cross Abstract: GPU kernels generated by large language model (LLM) agents can remain less efficient than expert implementations, but runtime alone does not reveal ho

Decoupling Memory from Context: Structured Memory for Token-Efficient Test-Time Continual Learning

arXiv:2610.02687v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in enterprise, scientific, and medical applications, where agents must incorporate domain-specifi

DeReAct: Decomposed Reasoning and Acting for Reliable AI Agents

arXiv:2610.02351v1 Announce Type: new Abstract: ReAct-based agents typically rely on a single LLM policy to propose actions, interact with the environment, and decide when a task is complete. This cou

Drive vs. Decay: On the Training Dynamics of Joint-Embedding Predictive Architectures

arXiv:2610.02344v1 Announce Type: new Abstract: Joint-Embedding Predictive Architectures (JEPAs) are prone to representation collapse, typically mitigated through empirical heuristics. We develop an e

researcharxiv-cs-lg

DuoMatching: Joint-Marginal Distribution Matching for Few-Step Video Generation

arXiv:2610.03543v1 Announce Type: new Abstract: Streaming video generation has benefited from distribution matching distillation (DMD), which matches the joint distribution of video frames to a video

safetyarxiv-cs-cv

DyadMem: A Long-Term Memory Benchmark of How Agents Work with Users

arXiv:2610.03020v1 Announce Type: new Abstract: Long-term agents must remember not only what is true about a user, but also how a particular agent should work with that user as their shared history ev

Enrich-on-Graph: Query-Graph Alignment for Complex Reasoning with LLM Enriching

model-releasesarxiv-cs-cl

arXiv:2509.20810v2 Announce Type: replace Abstract: Large Language Models (LLMs) exhibit strong reasoning capabilities in complex tasks. However, they still struggle with hallucinations and factual er

Found but Not Read: When Extracted Text Closes the Retrieval-Reading Gap in Document Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2610.02880v1 Announce Type: new Abstract: Retrieval-augmented document question answering assumes that once the right page is found, a vision-language model (VLM) can read it. We show that this

From Retrieval to Typed Decisions: Calibrated System One Models from Biomedical Sentence Encoders

arXiv:2610.02486v1 Announce Type: cross Abstract: Typed decision models answer schema-constrained questions about a text in one forward pass and return probabilities meant to be thresholded. We ask wh

FSPO: Policy-Consistent Risk and Pareto-Feasible Control for Budgeted LLM RL Post-Training

arXiv:2610.02828v1 Announce Type: new Abstract: Adaptive LLM reinforcement-learning post-training changes multiple training actuators online, including rollout temperature, group size, clipping, KL re

safetyarxiv-cs-ai

Get a GRIP, this will be a long TRIP: A Quantifiable Long-Range Framework for Verifying Over-squashing

arXiv:2610.03556v1 Announce Type: new Abstract: Empirical claims about the connection between over-squashing and long-range interactions in GNNs, can only be trusted if the benchmarks used to validate

HazardWeaver: Scientific Route Selection for Hazard Analysis Agents

arXiv:2610.03591v1 Announce Type: new Abstract: Understanding and assessing natural hazards is essential for disaster preparedness and risk reduction. Recent advances in large language models have spu

Jumping the Line: Exploiting Length Predictions in LLM Scheduling

arXiv:2610.03430v1 Announce Type: new Abstract: Efficient request scheduling is increasingly important for reducing completion time in large language model (LLM) serving. Size-based policies such as S

applicationsarxiv-cs-ai

Learning to Assess Heartbeat Observability for mmWave Heart-Rate Sensing

arXiv:2610.03570v1 Announce Type: new Abstract: Contactless heart-rate sensing with millimeter-wave (mmWave) radar requires assessing whether individual measurements support reliable estimation. We st

applicationsarxiv-cs-ai

Learning When to Commit from Partial Speech for End-to-End Simultaneous Speech Translation

arXiv:2610.02612v1 Announce Type: new Abstract: Simultaneous speech translation must emit useful target text before the source is complete while preserving every committed token. We adapt a full-utter

Local Ai

LiteEMG-FM: An Efficient and Deployable Foundation Model for Robust EMG Sensing

arXiv:2610.02497v1 Announce Type: new Abstract: Electromyography (EMG) signals vary substantially across individuals, body regions, recording sessions, and sensing hardware, limiting the generalizatio

local-aiarxiv-cs-lg

Agents

Lost in the Request: How Communication Variation Disrupts Retrieval and Action in Email Agents

arXiv:2610.02627v1 Announce Type: new Abstract: An email assistant should not complete less work simply because a user phrases the same request differently. Yet most benchmarks test each task with onl

agentsarxiv-cs-ai

Low-Cost Video--Time Priors as a Strong Baseline for EEG--fNIRS Emotion Regression on Familiar Videos

arXiv:2610.03618v1 Announce Type: new Abstract: Continuous emotion regression estimates moment-to-moment valence and arousal while a viewer watches a video. In familiar-video deployment, responses fro

Mapping and Advancing the Scalability-Accuracy Frontier of Nonlinear Causal Discovery

arXiv:2610.03258v1 Announce Type: cross Abstract: Scalable nonlinear causal discovery requires methods that combine flexible mechanism estimators with efficient search over large graph spaces. Several

applicationsarxiv-cs-ai

Mind the Refinement Gap: When Safe High-Level Robot Plans Produce Unsafe Executions

arXiv:2610.02662v1 Announce Type: new Abstract: Language-enabled robot systems increasingly combine semantic-graph planning with temporal-logic safety monitors. We investigate a trace-completeness ass

safetyarxiv-cs-lg

Mixture-of-Experts for Cryptocurrency Order Execution: Training Stability, Tail Risk, and Failure Modes

arXiv:2610.03369v1 Announce Type: cross Abstract: Deep reinforcement-learning policies for order execution can vary substantially across training seeds, so apparent architectural gains may reflect fav

Modeling Shared and Individual Structure for Cross-Subject Continuous Affect Regression from EEG-fNIRS

arXiv:2610.02796v1 Announce Type: new Abstract: Continuous, second-by-second valence-arousal estimation from physiological signals is typically studied in a subject-dependent setting, where the model

MOF-VERIFY: A Failure-Aware Agentic Harness for MOF Hypothesis Verification

arXiv:2610.03056v1 Announce Type: new Abstract: Large language models are increasingly used as reasoning components in AI-driven materials Co-Scientists, yet the reliability of the resulting verificat

Open-Endedness Bench: Measuring Epistemic Process from Agent Records

arXiv:2610.02588v1 Announce Type: new Abstract: Agents are increasingly given open-ended research tasks: discovering an empirical law from self-designed experiments, improving a heuristic whose optimu

Agents

Permutation Robustness Is Not Enough: Action Collapse in Multi-Agent Transformer Policies

arXiv:2610.02848v1 Announce Type: cross Abstract: Transformer policies are attractive for multi-agent robot learning because self-attention can model interactions among agents. However, multi-agent te

agentsarxiv-cs-lg

Positive-Unlabeled Learning for Agent Safety False Alarm Auditing

arXiv:2610.02925v1 Announce Type: new Abstract: Safety monitors help safeguard language-model agents interacting with external tools and environments, but conservative monitoring can generate many fal

safetyarxiv-cs-ai

Reasoning Models Are Accurate but Unsound on Identification

arXiv:2610.03519v1 Announce Type: new Abstract: A reasoning model asked whether a causal effect is recoverable from observational data can fail in two ways: it refuses an identifiable query or answers

Register-Routed Delayed Fusion: Rewiring Shortcut-Prone Observation Fusion in Visuomotor Imitation

arXiv:2610.02813v1 Announce Type: new Abstract: Visuomotor imitation policies combine high-dimensional visual observations with compact signals such as proprioception, and their fusion topology determ

safetyarxiv-cs-ro

ReSCUE: Re-translation with Sentence Commitment for Unsegmented Long-Form Simultaneous Sign Language Translation

arXiv:2610.03022v1 Announce Type: cross Abstract: Simultaneous Sign Language Translation (SLT) is critical for real-time communication, yet existing methods remain largely confined to sentence-level,

applicationsarxiv-cs-cl

Rethinking What to Cache in Few-Step Diffusion Transformers: Solver-Aware Target Selection

arXiv:2610.03577v1 Announce Type: cross Abstract: Diffusion Transformers (DiTs) can generate high-quality images and videos, but generating each sample requires multiple costly DiT forward passes. Two

Local Ai

RIPPLE in Still Water: Zero-Shot Clustering in Federated Learning with Wavelet Scattering Transform

arXiv:2610.03054v1 Announce Type: new Abstract: Clustered Federated Learning (FL) partitions a client population into groups of similar local distributions and trains one specialized model per cluster

local-aiarxiv-cs-lg