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Benchmarking Literature Retrieval for a Model Organism: A Dictyostelium Case Study

model-releasesarxiv-cs-cl

arXiv:2610.03130v1 Announce Type: cross Abstract: Biological literature retrieval systems are often developed and evaluated using broad biomedical corpora and general-purpose search tasks. However, ma

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Agents

Beyond Correctness: Resolving Underspecification in Agentic Text-to-SQL

DGX agent

arXiv:2610.02739v1 Announce Type: new Abstract: Agentic Text-to-SQL systems can interact with users to clarify underspecified queries before generating SQL. However, a correct execution result does no

agentsarxiv-cs-cl

5 Oct 2026

Local Ai

Beyond Entropy: Self-Diagnostic Multi-Role Token Optimization for Video Reasoning

arXiv:2610.03400v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards has substantially advanced multimodal reasoning, yet it remains fundamentally limited by ambiguous token-

local-aiarxiv-cs-cv

Agents

Beyond Predefined Sinks: Security-Aware Dependency Analysis for LLM Agents

arXiv:2610.03014v1 Announce Type: cross Abstract: Large language model (LLM)-based agents increasingly connect model-generated decisions to security-sensitive software capabilities such as command exe

agentsarxiv-cs-ai

Beyond Random Splits: Evaluating Drug-Target Affinity Models Under Chemically and Biologically Motivated Distribution Shifts Copy

arXiv:2610.03456v1 Announce Type: new Abstract: Drug-target affinity (DTA) prediction is widely used to prioritize candidate compounds before costly experimental screening. DTA models are often compar

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

Beyond Single Videos: Benchmarking and Active Evidence Seeking for E-Commerce Cross-Video Reasoning

arXiv:2610.03099v1 Announce Type: cross Abstract: E-commerce videos are information-dense and frequently compared by consumers evaluating products and merchants assessing marketing strategies. However

Beyond Trained Models: Compiling GNNs for a Sound Explainer Benchmark

arXiv:2610.03526v1 Announce Type: cross Abstract: Explainers for Graph Neural Networks (GNNs) are commonly evaluated by their plausibility, i.e., how well their explanations recover a predefined groun

Bidirectional Voronoi-biased Exploration Curriculum for Reinforcement Learning

arXiv:2610.03395v1 Announce Type: new Abstract: Long-horizon tasks with sparse rewards pose an exploration bottleneck for goal-conditioned reinforcement learning: a policy started from the initial sta

safetyarxiv-cs-lg

Bifidelity Karhunen-Loeve Expansion Surrogate with Active Learning for Random Fields

arXiv:2511.03756v2 Announce Type: replace-cross Abstract: We present a bifidelity Karhunen--Loeve expansion (KLE) surrogate model for field-valued quantities of interest (QoIs) under uncertain inputs.

BioMol-MQA: A Multi-Modal Question Answering Dataset For LLM Reasoning Over Bio-Molecular Interactions

arXiv:2506.05766v2 Announce Type: replace Abstract: Retrieval augmented generation (RAG) has shown great power in improving Large Language Models (LLMs). However, most existing RAG-based LLMs are dedi

applicationsarxiv-cs-cl

BitNest: Bit-Nested Speculative Decoding for Memory-Efficient LLM Inference Acceleration

arXiv:2610.02800v1 Announce Type: new Abstract: Speculative decoding accelerates autoregressive generation by using a lightweight draft to propose multiple tokens for parallel verification. However, e

Bounded Reachability & Jailbreak Detection via Contraction-Constrained State Space Models

arXiv:2610.02853v1 Announce Type: new Abstract: Safety heads are lightweight classifiers attached to pretrained language models for flagging harmful inputs before generation. Their empirical detection

safetyarxiv-cs-ai

Local Ai

Bridging Frontier Reasoning and Robot Execution: From Autonomous Demonstration Generation to Dense Language Supervision

arXiv:2610.03615v1 Announce Type: new Abstract: Recent advances in frontier models enable robot manipulation from only a few demonstrations, but high inference latency limits their use for real-time r

local-aiarxiv-cs-ro

Bridging Research and Practice: A Systematic Evaluation of Generalist and Dermatology-Specific Models in Clinical Skin Lesion Classification

model-releasesarxiv-cs-cv

arXiv:2610.03193v1 Announce Type: new Abstract: The application of machine learning to dermatology has grown substantially in recent years, moving beyond proof-of-concept studies toward potential appl

Broken scale symmetries in undercomplete linear autoencoders

arXiv:2610.03640v1 Announce Type: new Abstract: Neural network loss landscapes have many symmetries, which are preserved by gradient flow but broken by finite-stepsize stochastic gradient descent (SGD

Budgeted Cache Repair for Cross-Context KV-Cache Reuse

arXiv:2610.02233v1 Announce Type: cross Abstract: Cross-context KV-cache reuse predicts a shared segment's keys and values under a new prefix instead of recomputing them, and has been reported to do s

Budgeted-GS: Real-Time Large-Scale Gaussian Splatting via Factoring LOD

model-releasesarxiv-cs-cv

arXiv:2610.03162v1 Announce Type: cross Abstract: 3D Gaussian Splatting achieves excellent visual quality with real-time rendering, but at the scale of entire cities it does not fit: a trained model c

Building Interpretable Feature Representations for Resume-Vacancy Matching by Distilling Production LLM Signals

arXiv:2610.03112v1 Announce Type: new Abstract: Matching candidates to vacancies is central to recruitment, and a recruiter needs to see why a candidate fits, not only a single opaque relevance score.

applicationsarxiv-cs-cl

CalCErt: Bin-wise Certification of Confidence Calibration in Medical Image Classification

arXiv:2610.03142v1 Announce Type: new Abstract: Deep neural networks remain vulnerable to adversarial perturbations, which can distort not only predictions but also confidence scores, undermining unce

researcharxiv-cs-cv

Can AI Understand the Language of Origami?

model-releasesarxiv-cs-cv

arXiv:2603.13856v3 Announce Type: replace-cross Abstract: Building AI systems that can plan, act, and create in the physical world requires more than pattern recognition. Such systems must reason abou

Can SGD Handle Heavy-Tailed Noise?

arXiv:2508.04860v2 Announce Type: replace-cross Abstract: Stochastic Gradient Descent (SGD) is a cornerstone of large-scale optimization, yet its theoretical behavior under heavy-tailed noise -- commo

Can We Trust LLMs on Memristors? Diving into Reasoning Ability under Non-Ideality

arXiv:2603.13725v2 Announce Type: replace Abstract: Memristor-based analog compute-in-memory (CIM) architectures provide a promising substrate for the efficient deployment of Large Language Models (LL

Capability Scaling-Down Laws for LLM Compression

arXiv:2610.02462v1 Announce Type: cross Abstract: LLM compression reduces inference costs and memory requirements, but selecting a method and configuration remains largely empirical because comparable

Capturing Dynamics: The 4D Facial Expression Intensity Dataset

arXiv:2610.02647v1 Announce Type: new Abstract: The estimation and analysis of facial expression intensity play a crucial role in affective communication and human-computer interaction. Previous resea

applicationsarxiv-cs-cv

Causal discovery identifies pathways linking physical activity to dementia risk in the UK BioBank

arXiv:2610.02221v1 Announce Type: cross Abstract: Physical Activity (PA) is consistently associated with lower risk of dementia, yet the mechanism linking PA to dementia prevention remain incomopletel

researcharxiv-cs-ai

Tutorials

Causal Representation Learning with Instantaneous and Lagged Relations via Nonstationarity

arXiv:2610.03452v1 Announce Type: new Abstract: Causal representation learning for time-series data aims to identify latent states and their causal relations from observations. In this setting, an imp

tutorialsarxiv-cs-lg

Cephalonauts One: A deep fMRI dataset for decoding naturalistic speech in the human brain

arXiv:2610.03558v1 Announce Type: cross Abstract: Cephalonauts One is a whole-brain 3 Tesla (3T) functional magnetic resonance imaging (fMRI) dataset recorded while subjects listened to audio podcasts

Certified Mechanistic Edits: Behavioral Guarantees for Skill Removal and Preservation

arXiv:2610.03502v1 Announce Type: cross Abstract: Mechanistic edits (ablations, weight edits, activation steering) are the standard tools for unlearning a harmful capability from a neural network whil

applicationsarxiv-cs-ai

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

CHASE-VLA: Post-Training Quantization Framework for Vision-Language-Action Models with Chunk-Aware Scale Estimation

arXiv:2610.02666v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models map visual observations and language instructions to continuous robot actions, but a diffusion-based action expert (

safetyarxiv-cs-cv

Agents

Choosing Before Acting: Comparative Value Estimation for Long-Horizon Tool-Use Agents

arXiv:2610.02330v1 Announce Type: new Abstract: Large language models (LLMs) rely on long-horizon tool invocation sequences for complex tasks, where each invocation can alter the task state and condit

agentsarxiv-cs-ai

ChromaGS: Text-Driven Semantic Editing of 4D Gaussian Avatars

arXiv:2610.03441v1 Announce Type: new Abstract: We present ChromaGS, a method for real-time, language-guided color editing of animatable 3D Gaussian head avatars. Given a trained animatable avatar, us

researcharxiv-cs-cv

ChronoSpike: An Adaptive Spiking Graph Neural Network for Dynamic Graphs

arXiv:2602.01124v4 Announce Type: replace Abstract: Dynamic graph representation learning requires capturing both structural relations and temporal evolution, yet existing approaches face a core trade

ClarifyCodeBench: Evaluating LLMs on Clarifying Ambiguous Requirements for Code Generation

arXiv:2607.00711v2 Announce Type: cross Abstract: Large Language Models have emerged as programming assistants. However, the efficacy of code generation is constrained by the quality of input requirem

Tutorials

CLEAN: Psychometrically Consistent Incremental Cognitive Diagnosis under Concept-Space Expansion via Architectural Isolation

arXiv:2610.02278v1 Announce Type: new Abstract: Cognitive diagnosis (CD) is a fundamental task in intelligent education that profiles learner proficiency over knowledge concepts. In real-world learnin

tutorialsarxiv-cs-lg

CLIMB: Confidence-Guided Complementary Evidence for Multimodal Retrieval-Augmented Generation

arXiv:2610.03421v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have shown strong visual reasoning abilities, but knowledge-intensive visual question answering often requires

safetyarxiv-cs-cl

Clinical Concept Centers in LLMs

arXiv:2610.02829v1 Announce Type: new Abstract: Large language models are increasingly used in clinical settings. However, research into the reliability and performance of these models has focused alm

Closing the Train-Test Gap in World Models for Gradient-Based Planning

arXiv:2512.09929v2 Announce Type: replace Abstract: World models paired with model predictive control (MPC) can be trained offline on large-scale datasets of expert trajectories and enable generalizat

Co-design Gym: A Unified Benchmark for Embodiment-Policy Co-optimization

arXiv:2610.02366v1 Announce Type: new Abstract: Finding an optimal behaviour policy within a given environment is a widely studied problem in domains as diverse as games, robotics, energy infrastructu

Hardware

Coco: An Agentic Copilot for the Hardware--Software Co-Design Lifecycle

arXiv:2610.02376v1 Announce Type: cross Abstract: Co-designing ML models and the accelerators that run them is an unusual reasoning task: architects must draw confident, high-stakes conclusions about

hardwarearxiv-cs-ai

Hardware

Cocoon: A System Architecture for Differentially Private Training with Correlated Noises

arXiv:2510.07304v2 Announce Type: replace-cross Abstract: Machine learning (ML) models memorize and leak training data, causing serious privacy issues to data owners. Training algorithms with differen

hardwarearxiv-cs-ai

Agents

Coherence-Driven Belief Formation and Population Dynamics of Contagion in LLM Agents

arXiv:2610.02654v1 Announce Type: new Abstract: Models of social contagion usually assume how individuals adopt beliefs and derive population behavior from it. We instead empirically measure belief ad

agentsarxiv-cs-ai

Collective Bias Mitigation via Model Routing and Collaboration

arXiv:2610.03240v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in public health, finance, and governance, requiring both accuracy and societal value alignment.

safetyarxiv-cs-cl

CoLMbo-SV: A Grounded Language Model for Explainable Speaker Verification

arXiv:2609.33212v2 Announce Type: replace Abstract: Speaker verification systems achieve high accuracy but provide little account of the acoustic evidence behind their judgments. Making these systems

CoMemNet: A Continual Memory Network with Drift-Aware Sampling for Traffic Prediction

arXiv:2605.05738v2 Announce Type: replace-cross Abstract: Traffic sensor networks evolve as sensors are added and traffic distributions change, whereas most forecasting models assume a fixed node set

researcharxiv-cs-ai

Compound AI System Reliability: A Failure Taxonomy and Resilience Pattern Catalog from 150 Production Incidents

arXiv:2610.02503v1 Announce Type: cross Abstract: Deploying compound AI systems reliably and safely requires understanding failure modes that emerge at component boundaries, not within individual mode

applicationsarxiv-cs-ai

Conditional Capacity and Routing in Mixture-of-Experts Particle Transformers

arXiv:2610.02701v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models can increase parameter capacity without proportionally increasing active computation, but it is unclear how this trade-o