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TextualRL: Textual Reinforcement Learning for Context Management

arXiv:2610.18216v1 Announce Type: cross Abstract: LLM agents can adapt to task feedback by revising the textual context that guides their behavior, without changing their model parameters. However, tu

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TextVerifier: Seeing Beyond Recognition to Verify Chinese Text Rendering

DGX agent

arXiv:2610.19551v1 Announce Type: new Abstract: Text-to-image models increasingly generate readable Chinese text that still contains local rendering defects, such as a character with an extra or missi

model-releasesarxiv-cs-cv

15 Oct 2026

The Accuracy Well: Nonmonotone Accuracy Dynamics at Vanishing Initialization

arXiv:2610.20439v1 Announce Type: new Abstract: We study classification dynamics under small initialization and identify a pronounced non-monotone behavior of accuracy that we call the accuracy well:

researcharxiv-cs-lg

The Agent Knew: Internal States Forecast Unauthorized Actions and Stop Them, and the Lies That Follow

arXiv:2610.17301v1 Announce Type: new Abstract: Language-model agents sometimes act beyond their authorization and then misreport what they did. On CAPE-Bench, matched agent tasks in which one fact de

The Agentic Scientist: Orchestrating Coding Agents for Automated Scientific (Co-)Discovery

arXiv:2610.18933v1 Announce Type: cross Abstract: Modern coding agents are fundamentally reshaping the role of human input for computational research: they write code, launch in silico experiments, an

agentsarxiv-cs-cl

The Anatomy of a Blind Spot: What an LLM Judge Must Be Shown, and How Much

arXiv:2610.15938v1 Announce Type: new Abstract: Evaluation of text generated by large language models (LLMs) is increasingly delegated to LLMs acting as judges, yet what such a judge must be shown rem

The Compiler May Read It, the Agent May Not: Keeping Part of a Research Code Away from a Coding Agent

arXiv:2609.35557v2 Announce Type: replace-cross Abstract: The compiler must read modules a physics-based solver cannot build without; the coding agent must not read that intellectual property. The har

agentsarxiv-cs-ai

The COUNT: Counting N-grams Unlocks Scaling on Host Memory and Training-Free Adaptation

arXiv:2610.20456v1 Announce Type: new Abstract: The scaling relationship of compute and performance for language models (LMs) has been extensively studied; more recently, models are being designed to

The Format is the Filter: Scoring Format Fixes What an LLM Judge Can Express

arXiv:2610.17158v1 Announce Type: new Abstract: When an LLM judge filters instruction-tuning data, its scoring format (binary verdict, 1--5 rating, 0--100 score, or comparison) is rarely justified or

safetyarxiv-cs-ai

The Future of AI-assisted Physics: Discovering, Verifying and Organizing Results via Formalization

arXiv:2610.14059v1 Announce Type: cross Abstract: Rapid advances in AI have opened a new era for physics, enabling unprecedented capabilities in research and discovery. However, these advances also br

The Gap Is in the Geometry: A Convergent Meta-Analysis of Multi-Facet Aerial and Remote Sensing Video Understanding

model-releasesarxiv-cs-cv

arXiv:2610.19102v1 Announce Type: new Abstract: Aerial and remote sensing video is increasingly evaluated like web video, by posing multiple-choice questions to multimodal large language models (MLLMs

The Geometry of Language Model Logits: Generic Structure and Learned Departures

arXiv:2610.18520v1 Announce Type: cross Abstract: Autoregressive language models produce, at every prediction step, a vocabulary-sized logit vector whose structure determines the conditional next-toke

Tutorials

The Impact of Detector Architecture, Model Scale, and Scene Density on Multi-Pedestrian Tracking

arXiv:2610.19369v1 Announce Type: new Abstract: Pedestrian tracking in video is important for applications such as surveillance and scene analysis, but it becomes more challenging in crowded scenarios

tutorialsarxiv-cs-cv

The Imperishable Record, Forgiveness, and the Future of Criminal Justice

arXiv:2610.15499v1 Announce Type: cross Abstract: Digital technologies can preserve records of wrongdoing indefinitely, and AI can give those records continuing influence over a person's future. We de

The Invisible Leash: Why RLVR May or May Not Escape Its Origin

arXiv:2507.14843v5 Announce Type: replace-cross Abstract: Recent advances highlight Reinforcement Learning with Verifiable Rewards (RLVR) as a promising approach to enhancing LLM capabilities. However

The Many Morals of AI: The cautionary bias of North American news narratives in global perspective

arXiv:2610.18573v1 Announce Type: cross Abstract: The stories told about artificial intelligence (AI) play a central role in shaping public understanding, policy debates, and expectations about techno

safetyarxiv-cs-ai

The minimal width of neural networks and immersions of real projective space

arXiv:2610.20229v1 Announce Type: cross Abstract: We investigate the minimal width of deep neural networks that can approximate all continuous functions from R^n to R^m in the topology of uniform conv

researcharxiv-cs-lg

Hardware

The Physics Is the Network: Fast, Training-Free Neural Emulation

arXiv:2610.20525v1 Announce Type: new Abstract: Neural emulation need not require learning a substitute for known physics. We investigate PolyNets that couple a continuous-time neural representation o

hardwarearxiv-cs-lg

The Reasoning Trap -- Logical Reasoning as a Mechanistic Pathway to Situational Awareness

arXiv:2603.09200v2 Announce Type: replace-cross Abstract: Situational awareness, the capacity of an AI system to recognize its own nature, understand its training and deployment context, and reason st

The Sharp Directions Are Against You: Curvature Analysis of Activation Steering

arXiv:2610.16084v1 Announce Type: new Abstract: Activation steering adds a vector to a language model's internal representations at inference time. It is a lightweight alternative to fine-tuning for b

The Silent Failures in Agentic Data Science

arXiv:2610.14115v1 Announce Type: new Abstract: Large language models have significantly lowered the barrier to writing code. Naturally, this has also led to the development of data science agents tha

agentsarxiv-cs-ai

The Singularity Has a Distribution: Exact Timing Laws and Detection Limits for a Stochastic Intelligence Explosion

arXiv:2610.18445v1 Announce Type: new Abstract: Automating AI research creates a positive feedback loop between software capability and research effort. We study a stochastic realisation of this loop

The Smart Buildings Challenge: Learning Foundation Models for Real-World Optimization at Scale

arXiv:2610.18511v1 Announce Type: new Abstract: Buildings cause nearly 40% of carbon emissions in the US. Of total building emissions, HVAC systems are responsible for approximately 40-60% and account

safetyarxiv-cs-ai

The Sum of All Summaries: PROPALIGN for Graded Proposition Salience Evaluation

arXiv:2610.19387v1 Announce Type: new Abstract: Identification and ranking of salient propositions in documents is indirectly involved in many AI tasks, but few papers define proposition salience scor

researcharxiv-cs-cl

Theoretical and Practical Advances in Hierarchical Bayesian Deep Learning Methodology

model-releasesarxiv-cs-lg

arXiv:2610.19074v1 Announce Type: new Abstract: Reliable uncertainty quantification remains an important challenge in AI. Hierarchical Bayesian methods provide a principled framework for this by repre

Think in Strategies, Act with Tools: Traceable Agentic Optimization for Combinatorial Problems

arXiv:2610.14736v1 Announce Type: new Abstract: Optimization by PROmpting (OPRO) directly leverages Large Language Models (LLMs) to generate candidate solutions through natural-language prompting. How

agentsarxiv-cs-ai

Thinking Outside the Bots: Human Ideas in AI's Semantic Blind Spots Are Not Inferior

arXiv:2610.17324v1 Announce Type: cross Abstract: AI-generated outputs are frequently perceived as highly creative, but also tend to be more similar to each other (homogenized) than human ideas. Does

ThreadBench: Benchmarking Realism in Agentic Simulation of Real-world Discussions

arXiv:2606.14715v2 Announce Type: replace-cross Abstract: LLM agents are increasingly used to simulate real-world interactions, but it remains unclear whether simulated behaviors preserve the content

Applications

Tiered-Resolution Volumetric Mapping for Real-Time Simultaneous Planning and Mapping on Natural Outdoor Environments

arXiv:2610.20476v1 Announce Type: new Abstract: Real-time volumetric mapping for a planning robot is dominated by the cost of integrating and storing information at the finest resolution, yet only a s

applicationsarxiv-cs-ro

TIMERSI: Towards Self-Improved Agentic Deployment for Time-Series Foundation Models

arXiv:2610.19725v1 Announce Type: new Abstract: Time-series foundation models provide strong zero-shot forecast, but deployment for real- world applications still requires task-specific designs for mo

agentsarxiv-cs-lg

Titan-Llama: Retrofitting Pre-trained LLMs with External Neural Memory Modules

arXiv:2610.19210v1 Announce Type: new Abstract: Transformers are efficient to pre-train at scale because attention permits parallel computation across sequence positions, but long-context inference re

To Reason but Not to Stop: Understanding and Recovering Pruned Large Reasoning Models

arXiv:2610.19934v1 Announce Type: new Abstract: Weight pruning can degrade the accuracy of large reasoning models (LRMs), yet it remains unclear whether it damages reasoning itself. We find that much

researcharxiv-cs-cl

Token Utility for On-Policy Distillation via Local Trust-Region Optimization

arXiv:2610.18883v1 Announce Type: new Abstract: Token-selection methods for on-policy distillation (OPD) prioritize student-visited positions using diverse signals, including uncertainty, teacher-stud

safetyarxiv-cs-lg

Too Late to Prune: Generative AI, the Screen, and Team Creative Decisions

arXiv:2610.14984v1 Announce Type: cross Abstract: Generative AI makes options cheap and shifts the bottleneck of creative work from generating options to screening them. We argue that AI's value depen

Local Ai

TopicMerge: A Divide and Conquer Approach to Topic Modeling

arXiv:2610.19323v1 Announce Type: new Abstract: Recent advancements in topic modeling have split into two main paths: efficient embedding-based methods that offer scalability but are often outperforme

local-aiarxiv-cs-cl

Topology-Preserving Multi-Usage Pipe Centerline Refinement under Permanent Tubular Occupancy

arXiv:2610.19403v1 Announce Type: new Abstract: Discrete multi-group pipe routers place channels on a grid or coarse graph. The resulting centerlines are topologically feasible but typically contain m

safetyarxiv-cs-ro

TorchSpin: Differentiable Electron Paramagnetic Resonance Simulation in PyTorch

model-releasesarxiv-cs-lg

arXiv:2610.18924v1 Announce Type: cross Abstract: Interpreting electron paramagnetic resonance (EPR) spectra commonly requires repeated forward simulation and optimization of spin Hamiltonian paramete

Toward a Universal Characterization of Sudoku Puzzle Difficulty

arXiv:2507.21137v2 Announce Type: replace Abstract: In this paper we try to answer the question 'What constitutes Sudoku difficulty rating across different Sudoku websites?' Using two distinct methods

Local Ai

Toward Fine-Grained Forgetting:Attribute Unlearning for Multimodal Large Language Models

arXiv:2608.01008v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) can memorize and disclose sensitive information, motivating machine unlearning to remove designated knowled

local-aiarxiv-cs-ai

Toward System One Clinical Intelligence: Parameter-Efficient Fine-Tuning and Counterfactual Explainability of Decision Foundation Models

arXiv:2610.15029v1 Announce Type: cross Abstract: Automated disease classification from patient-reported symptoms requires balancing the speed of lightweight discriminative models against the contextu

Towards a Benchmark for Fire Safety Code Interpretation in the Hong Kong AEC Industry

arXiv:2610.16127v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly explored in the Architecture, Engineering, and Construction (AEC) domain, yet their reliability in regul

Towards a Data-Centric Understanding of Spatial Reasoning in Multimodal Large Language Models

arXiv:2610.19108v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have been widely applied in embodied agents and robotics for vision tasks. However, MLLMs suffer from limitat

Towards Agentic Robots: Model, Data, Environment, and Harness

arXiv:2610.20442v1 Announce Type: new Abstract: Foundation models are driving a paradigm shift in robotic intelligence, moving beyond low-level action prediction to goal-directed orchestration of data

agentsarxiv-cs-ro

Local Ai

Towards Certifiable State Estimation under Observability Degeneracy

arXiv:2610.20490v1 Announce Type: new Abstract: Certifiable optimization provides global optimality guarantees for non-convex state estimation in robotics. However, under severe physical sensing degen

local-aiarxiv-cs-ro

Towards Efficient Simultaneous Inverse Text Normalization with Pretrained Text-to-Text Language Model and Read-Tag-Write Policy

arXiv:2610.19492v1 Announce Type: new Abstract: Streaming Inverse Text Normalization (ITN) is vital for converting spoken-form outputs from streaming Automatic Speech Recognition into formatted writte

safetyarxiv-cs-cl

Towards Merging-conditional Backdoor Unalignment against Large Language Models

arXiv:2610.14701v1 Announce Type: cross Abstract: Model merging has become widely adopted for enhancing the task-specific performance of Large Language Models (LLMs) without additional training. While

Applications

Towards Resilience: Slice-Driven Orchestration of Mobile IoMT in 6G-Edge-Continuum

arXiv:2610.17296v1 Announce Type: new Abstract: Emerging healthcare services, such as real-time diagnostics, remote health monitoring and computer-assisted surgery, require stringent Quality of Servic

applicationsarxiv-cs-ai

Applications

Towards Training and Inference of Graph Neural Networks in Graph Databases

arXiv:2610.19142v1 Announce Type: new Abstract: Graph Neural Networks on large graphs commonly rely on in-memory graph storage with ad hoc data processing pipelines for both training and inference. Gr

applicationsarxiv-cs-lg