Search: “evaluation”

The Judge Is Not Its Twin: Post-training makes a model's writing more predictable but barely moves its taste, as a judge, toward predictable writing

arXiv:2609.32196v1 Announce Type: cross Abstract: Language models are now routinely graded by other language models. If post-training makes a model's own writing more predictable, it may also teach th

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Safety

The Model Knows When to Stop: Training-Free Early Stopping for Long-Context Reading

DGX agent

arXiv:2609.34590v1 Announce Type: cross Abstract: Language models often process long inputs sequentially in chunks, but continuing to read after sufficient evidence has been acquired wastes computatio

safetyarxiv-cs-lg

29 Sept 2026

The Temporal Tug-of-War: Visualizing and Detecting RAG Conflicts in Diffusion Models via Trajectory Variance

arXiv:2609.31684v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) introduces a specific failure mode in discrete diffusion language models: when retrieved context contradicts para

Theoretical Refinement of CLIP by Utilizing Linear Structure of Optimal Similarity

arXiv:2510.15508v2 Announce Type: replace Abstract: In this study, we propose an enhancement to the similarity computation mechanism in multimodal contrastive pretraining frameworks such as CLIP. Prio

Theory of Scene: Breaking the Symmetry Trap in Multi-Agent LLM Coordination

arXiv:2609.32939v1 Announce Type: new Abstract: Multi-agent systems built on large language models (LLMs) are largely homogeneous, as their agents behave alike even across distinct LLMs. We show that

Think Fast, Plan Selectively: Adaptive Deliberation for Efficient Data-Driven MPC

arXiv:2609.32591v1 Announce Type: cross Abstract: Data-driven model predictive control (MPC) combines learned world models with online trajectory optimization, achieving strong performance in continuo

ThinkNet: Compact Architecture Selection and Validation-Gated Ensembles for Subject-Independent MI-EEG Decoding

arXiv:2609.33967v1 Announce Type: cross Abstract: Practical assistive and rehabilitative brain--computer interfaces require subject-independent motor-imagery EEG (MI-EEG) decoders that generalize to n

Tilted Schrodinger Bridge Matching

arXiv:2609.34642v1 Announce Type: new Abstract: Schrodinger bridges provide an entropy-regularized framework and a principled solution for unpaired domain translation. In practice, a pretrained bridge

TMCS: Tool-Grounded Multi-Agent Reasoning for Compositional Chemical Problem Solving

arXiv:2609.35336v1 Announce Type: cross Abstract: Despite the promise of Large Language Models (LLMs) in computational chemistry, rigorous combinatorial chemistry problems remain difficult because the

agentsarxiv-cs-cv

TokenCast: Forecasting Token Consumption During LLM Agent Execution

arXiv:2609.35760v1 Announce Type: new Abstract: When a large language model (LLM) agent executes the same task, token consumption can vary by over an order of magnitude across runs. The agent chooses

Toward On-Chip Training of Spiking Neural Networks for Dense Event-Based Vision

arXiv:2609.32405v1 Announce Type: new Abstract: Event cameras provide low-latency, asynchronous visual sensing for resource-constrained robotics. Spiking neural networks (SNNs) process event streams n

Towards Autonomous and Auditable Medical Imaging Model Development

arXiv:2607.10522v2 Announce Type: replace Abstract: Large language model (LLM) agents are beginning to automate machine learning engineering (MLE) by coupling planning, code execution, debugging, and

agentsarxiv-cs-cv

Towards Eliminating Catastrophic Forgetting in the Curriculum Learning of Math Reasoning Tasks

arXiv:2609.33655v1 Announce Type: new Abstract: Curriculum learning has found broad application across numerous domains. Nevertheless, its effectiveness is intrinsically curtailed by catastrophic forg

TRACE: Trajectory-Based Safety Patch Learning for LLM Post-Training Realignment

arXiv:2607.16242v2 Announce Type: replace Abstract: Fine-Tuning-as-a-Service (FTaaS) platforms let users perform supervised fine-tuning (SFT) on customized data, but this pipeline can erode model safe

Traceable Human-to-Humanoid Sign Language Benchmarking

model-releasesarxiv-cs-ro

arXiv:2609.33354v1 Announce Type: new Abstract: Sign data collection is costly, and teleoperation scales poorly, motivating reuse of large video corpora. Humanoid signing requires converting video-der

Transfer Learning for Edge Classification on Dynamic Text-Attributed Graphs

arXiv:2609.32849v1 Announce Type: new Abstract: Learning transferable representations for dynamic text-attributed graphs (DyTAGs) requires models to capture underlying interaction dynamics that persis

Local Ai

Trapped by Their Own Rollouts: Understanding Aggregation--Rollout Feedback in Federated On-Policy Distillation

arXiv:2609.32573v1 Announce Type: new Abstract: On-policy distillation (OPD) is a promising approach to language-model adaptation, aligning teacher supervision with the student's own generated traject

local-aiarxiv-cs-lg

Two-Stage Multi-View Gait Recognition with a Re-Embedding Network

arXiv:2609.32244v1 Announce Type: new Abstract: Gait recognition always remains challenging due to severe overfitting and the rigid view constraints common in single-stage approaches. We propose a two

UMR: Universal Manipulation Representation

model-releasesarxiv-cs-ro

arXiv:2609.34256v1 Announce Type: new Abstract: General-purpose embodied manipulation hinges on a unified action representation that generalizes across embodiments and scales readily. Yet existing pol

Understanding the Synergy between SFT, RLVR, and OPD in LLM Post-Training

arXiv:2609.31900v1 Announce Type: new Abstract: Modern LLM post-training composes supervised fine-tuning (SFT), reinforcement learning with verifiable rewards (RLVR), and on-policy distillation (OPD)

Tutorials

Uni-VLaT: Whole-Body Tactile Adaptation of VLA Policies for Humanoid Loco-Manipulation

arXiv:2609.35450v1 Announce Type: new Abstract: Physical contact often determines how a humanoid should respond during loco-manipulation, yet vision and proprioception alone are often insufficient to

tutorialsarxiv-cs-ro

UniBYD: A Unified Framework for Learning Robotic Manipulation Across Embodiments Beyond Imitation of Human Demonstrations

model-releasesarxiv-cs-ro

arXiv:2512.11609v3 Announce Type: replace Abstract: In embodied intelligence, the embodiment gap between robotic and human hands brings significant challenges for learning from human demonstrations. A

Unified Trajectory Matching Policy Optimization: Diverse T2I Generation and VLA Generalization

arXiv:2609.34688v1 Announce Type: new Abstract: Reward-maximizing reinforcement learning (RL) is widely used to post-train stochastic diffusion and flow policies for text-to-image (T2I) generation. Ho

safetyarxiv-cs-cv

Unified Visual-Tactile-Action Modeling from Human Demonstrations for Dexterous Manipulation

arXiv:2609.34182v1 Announce Type: new Abstract: Dexterous manipulation requires tactile feedback.However, robot tactile demonstrations are difficult to scale,because dexterous-hand teleoperation provi

researcharxiv-cs-ro

Tutorials

Unifying Deep Predicate Invention with Pre-trained Foundation Models

arXiv:2512.17992v2 Announce Type: replace Abstract: Long-horizon robotic tasks are hard due to continuous state-action spaces and sparse feedback. Symbolic world models help by decomposing tasks into

tutorialsarxiv-cs-ro

UNMATCH: Selective Unbalanced Token-Patch Matching for Forensic Image-Claim Verification

arXiv:2609.31766v1 Announce Type: new Abstract: Contextual image misuse pairs an image with a misleading claim. We study image-claim correspondence in fact-checked pairs containing out-of-context reus

UOPD: Uncertainty-Aware Intervention for On-Policy Distillation of Multi-Turn Agents

arXiv:2609.34036v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a student on its own rollouts using dense supervision from a teacher. In multi-turn environments, a mistake at a cri

Using Machine Learning to Investigate Predictors of Fasting Blood Glucose: Insights into Circadian Timing and Age Interactions

arXiv:2609.32386v1 Announce Type: new Abstract: Impaired glucose regulation is a major contributor to metabolic dysfunction and type 2 diabetes. This study developed an interpretable machine-learning

VastMAT: A Large-Scale Multi-Category Benchmark for Multi-Animal Tracking

arXiv:2609.34390v1 Announce Type: new Abstract: Multi-animal tracking (MAT) supports the study of animal movement, behavior, and group interactions. However, general multi-object tracking (MOT) benchm

VehDyn: A Driving World Model Benchmark for Vehicle Dynamics

arXiv:2609.33264v1 Announce Type: new Abstract: Video world models are emerging as data engines, action planners, and generative simulators for autonomous driving, but existing benchmarks primarily as

Verifiable Visual Rewards Transfer from Synthetic Scenes to Natural Prompts

arXiv:2609.35641v1 Announce Type: cross Abstract: Precise instruction following in image generation, such as satisfying object counts and spatial relations, remains an open challenge at least in part

Verifying Neural Networks with Reinforcement Learning

arXiv:2609.34553v1 Announce Type: new Abstract: Formal verification can play a key role in ensuring the reliability of Deep Neural Networks (DNNs) deployed in safety-critical systems. Modern DNN verif

Applications

Video Captioning in Low-Light Conditions through Efficient Uncertainty-Aware Caption Correction

arXiv:2609.31697v1 Announce Type: new Abstract: Low-light conditions can significantly degrade the ability of vision-language models (VLMs) to accurately describe human actions in videos. In this work

applicationsarxiv-cs-cv

Video, Ergo Genero: Unifying Video Tasks via Spatiotemporal Analogy

arXiv:2609.33935v1 Announce Type: new Abstract: Adapting video models to new tasks typically requires dedicated data curation and fine-tuning. While visual analogy provides a training-free alternative

VideoPhysEdit: Physical Counterfactual Video Editing via Rigid-Body Physical Scene Reconstruction

arXiv:2609.35134v1 Announce Type: new Abstract: Video editing has advanced substantially in recent years, with methods increasingly accounting for the visual consequences of edits, such as changes to

Vision-Language Agents for Active Perception in Optics Laboratories

arXiv:2609.32918v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly being used in scientific workflows, but their ability as agents to directly control laboratory experime

agentsarxiv-cs-cv

VisionLogic: Discovering and Grounding Decision-Relevant Visual Concepts

arXiv:2503.10547v3 Announce Type: replace Abstract: Concept-based explanations help users understand vision models through recognizable visual patterns. However, existing methods often rely on correla

VLM-Guided Experience Replay

arXiv:2602.01915v2 Announce Type: replace Abstract: Recent advances in Large Language Models (LLMs) and Vision-Language Models (VLMs) have enabled powerful semantic and multimodal reasoning capabiliti

agentsarxiv-cs-lg

Tutorials

VPTwin: Real-Sim-Real Video Prediction for Robotic Manipulation Planning

arXiv:2609.33104v1 Announce Type: new Abstract: While action-conditioned video prediction provides an intuitive world model for robotics, purely data-driven predictors often suffer from compounding er

tutorialsarxiv-cs-ro

Applications

WB-WAM: Heterogeneous Body-Hand Pre-training for Humanoid Loco-Manipulation

arXiv:2609.34199v1 Announce Type: new Abstract: Humanoid loco-manipulation demands coordinated body and hand behavior, while conventional robot pre-training data provide limited coverage of such whole

applicationsarxiv-cs-ro

Weighted Spline-Expanded Networks with Distributional Balancing for Continuous Treatment Effects

arXiv:2609.33740v1 Announce Type: cross Abstract: Estimating causal effects with continuous treatments in observational studies is challenging due to confounding, model misspecification, and high-dime

Local Ai

What Do Latent Predictive Vehicle Representations Retain? Measuring State, Geometry, and Local Response

arXiv:2609.32512v1 Announce Type: new Abstract: Models of vehicle dynamics learned from logged states and commands complement physics-based models, and latent world models, which predict in a learned

local-aiarxiv-cs-lg

Applications

What Makes World Action Models Generalize? An Empirical Study of Test-Time Future Modeling

arXiv:2609.34981v1 Announce Type: new Abstract: World action models (WAMs) predict the future alongside actions during training. Due to the heavy computation cost of video denoising, whether the futur

applicationsarxiv-cs-cv

What masking geometry works best for EEG foundation models?

arXiv:2609.33487v1 Announce Type: new Abstract: EEG foundation models hold promise for scalable brain-signal decoding across clinical and cognitive neuroscience applications, yet their pre-training pi

What Should the Reflector See? An Empirical Study of Evidence in Reflective Prompt Optimization

arXiv:2609.32452v1 Announce Type: cross Abstract: Reflective prompt optimization revises instructions using examples of a model's behavior, but which evidence the reflector should receive remains uncl

What Stops Recursive Self-Improvement in Robotics? Lessons from 123 Rounds of Agentic Skill Discovery

arXiv:2609.31760v1 Announce Type: cross Abstract: Can a robot improve itself the way coding agents now improve software? We built an agentic system to find out. It watches a robot fail, works out whic

agentsarxiv-cs-lg

When Does Domain Adaptation Help on Physical Vibration Sensors? A Held-Out-Bearing Study of Neural-Operator and Convolutional Models

arXiv:2609.31639v1 Announce Type: new Abstract: Diagnosing rolling-element bearing faults from vibration is a canonical physical-sensing task and a widely used benchmark for domain adaptation under op

When Known Physics Helps Neural PDE Models: Residual Constraints Out-Regularize Generic Priors for Nonlinear Dynamics

arXiv:2609.34012v1 Announce Type: new Abstract: Neural PDE surrogates increasingly incorporate structural priors, yet it is often unclear whether their gains arise from physics-specific information or