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Evaluating and Improving Evidence-Grounded Fact-Checking in LLMs via Multi-Round Evidence Ablation

arXiv:2609.08943v1 Announce Type: cross Abstract: Automatic fact-checking systems assess the veracity of claims given evidence from relevant documents. Large Language Models (LLMs) have demonstrated s

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Model Releases

Evaluating Deep-Search Agents under Hierarchical Web Evidence Poisoning

DGX agent

arXiv:2609.06027v1 Announce Type: cross Abstract: Search-augmented LLM agents are increasingly used for consumer decisions, making them vulnerable to Generative Engine Optimization (GEO) poisoning. Ex

model-releasesarxiv-cs-ai

10 Sept 2026

Evaluating Model Retraining under Drift: Paired Comparisons of Cumulative Subgroup Disparity

arXiv:2609.09788v1 Announce Type: new Abstract: Choosing when to retrain a deployed classifier requires assessing subgroup error rates across the sequence of models used, including periods between upd

safetyarxiv-cs-lg

Evaluating Steering Techniques using Human Similarity Judgments

arXiv:2505.19333v2 Announce Type: replace Abstract: Current evaluations of Large Language Model (LLM) steering techniques focus on task-specific performance, overlooking how well steered representatio

Evaluating the Scalability and Adversarial Generalization of GRPO-Trained NLI Models

arXiv:2504.18376v3 Announce Type: replace-cross Abstract: Natural Language Inference (NLI) is a central task in natural language understanding with applications in fact-checking, question answering, a

Event Interaction in Low-Rank Bottlenecks for Temporal Relation Extraction

arXiv:2609.06731v1 Announce Type: cross Abstract: Temporal relation extraction determines whether an event occurs before, after, or simultaneously with another event, and therefore relies on accuratel

Every Activation Boosted: Scaling General Reasoner to 1 Trillion Open Language Foundation

arXiv:2510.22115v2 Announce Type: cross Abstract: We introduce Ling 2.0, a series reasoning-oriented language foundation built upon the principle that every activation boosts reasoning capability. Des

“Everyone killed” still seems to me to be an unrealistic exaggeration; I still don’t see a plausible path to that.

“Everyone killed” still seems to me to be an unrealistic exaggeration; I still don’t see a plausible path to that. OpenAI and Anthropic researchers agree: their approach to AI development could get ev

safetygary-marcus--x

Everything in Moderation: Per-Domain Coverage Optima and Alignment-Resistant Domain Gaps in Multi-Domain Mid-Training

arXiv:2609.09081v1 Announce Type: new Abstract: Mid-training, the stage between pre-training and alignment, is where a model's per-domain data composition is typically set by data availability rather

Evidence-Aligned Entity Verification for Hallucination Detection in Retrieval-Augmented Generation

arXiv:2609.08267v1 Announce Type: new Abstract: Hallucination detection is crucial for large language models (LLMs), as hallucinated content creates significant barriers in applications requiring fact

Local Ai

Evidence-Aligned Local Composition of Discrete Experts for Sequence Restoration

arXiv:2609.05801v1 Announce Type: new Abstract: A document modeled as a discrete sequence of tokens can be thought of as being generated from a composition of texts from different domains; a README fi

local-aiarxiv-cs-ai

Evidence-Grounded Retrieval for Investigation Hunt Lead Generation from CTI Reports

arXiv:2609.08790v1 Announce Type: cross Abstract: Threat hunting increasingly depends on converting unstructured knowledge (e.g., Cyber Threat Intelligence reports) into actionable hunt leads: concise

Evidence-Order Calibration for Selective Visual Reasoning under Progressive Loss of Question-Critical Evidence

arXiv:2609.09184v1 Announce Type: new Abstract: Vision-language model (VLM) confidence may change in aggregate when visual evidence is degraded while remaining structurally inconsistent within individ

researcharxiv-cs-cv

Evidential-Based Higher-Order Set Argumentation Framework

arXiv:2608.27824v2 Announce Type: replace Abstract: Evidential argumentation extends Dung's abstract argumentation by requiring arguments and interactions to be backed by chains of evidence rooted in

EvolveScaler: Synthesizing Information-Evolution Contexts via Executable State Machines and Natural-Language Rendering

arXiv:2609.08435v2 Announce Type: new Abstract: In persistent interactions, long contexts may encode an evolving process rather than a fixed record: later events can revise or revoke earlier informati

Exact Degeneracy Under Balanced k-Shot Sampling:Consequences for Small-Sample Discriminant Analysis on LLM Embeddings

arXiv:2609.09860v1 Announce Type: new Abstract: Balanced k-shot sampling draws exactly k labeled examples per class. We show that it induces an exact, provable degeneracy in a family of small-sample d

Exact-Form Regret for Gradient Descent, Mirror Descent and Follow-the-Regularized-Leader

arXiv:2609.09466v1 Announce Type: new Abstract: Online gradient descent is usually studied through external regret, where the learner competes with fixed alternatives. Recent work shows that first-ord

Exclusive: Cfo.ai launches an agentic CFO for business founders

model-releasessiliconangle

Artificial intelligence startup Runway Financial Inc. said today it has decided to pivot away from accounting software and rebrand itself as cfo.ai. That decision marks the launch of its new flagship

ExecCritic: Learn to Test, Test to Improve for Coding Agents

arXiv:2609.09133v1 Announce Type: new Abstract: Execution feedback can guide coding agents toward correct repository repairs, but only when the tests capture the behavior requested by the issue. Agent

Experiences people can interact with in real time. A new kind of foundational moment. A sneak peek into what's possible and what's coming fo…

model-releasescristobal-valenzuela--x

Experiences people can interact with in real time. A new kind of foundational moment. A sneak peek into what's possible and what's coming for real-time video generation. We believe instant generation

Experimental Analysis of Productive Interaction Strategy with ChatGPT: User Study on Function and Project-level Code Generation Tasks

arXiv:2508.04125v2 Announce Type: replace-cross Abstract: The application of Large Language Models (LLMs) is growing in the productive completion of Software Engineering tasks. Yet, studies investigat

ExpertLens: Visualizing Embedding Spaces for Post-Hoc Explainability in MoE Enhanced Retrievers

arXiv:2609.06155v1 Announce Type: cross Abstract: Neural models, including dense retrievers, have been widely adopted in Information Retrieval (IR), often delivering state-of-the-art performance. Desp

Explainable Deep Learning for Price-Trade Dynamics: From Black-Box Forecasts to Effective Parametric Models

arXiv:2609.06085v1 Announce Type: cross Abstract: Understanding the joint dynamics of prices and trades is central to market microstructure, where returns and order flow interact through nonlinear and

Explainable Token-level Noise Filtering for LLM Fine-tuning Datasets

arXiv:2602.14536v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have seen remarkable advancements, achieving state-of-the-art results in diverse applications. Fine-tuning, an im

Agents

Explaining AI Agents Through Execution Traces

arXiv:2609.06063v1 Announce Type: new Abstract: AI Agents are increasingly deployed in real-world settings, where they interact with external tools and make sequential decisions with limited human ove

agentsarxiv-cs-ai

Explaining f-Divergence-Based Regularization via Local Curvature and Sharpness-Aware Minimization

model-releasesarxiv-cs-lg

arXiv:2609.09367v1 Announce Type: new Abstract: Divergence-based regularization and Sharpness-Aware Minimization (SAM) are two prominent approaches for improving generalization in deep learning, both

Exploring Bottom-Up Clustering for Creating Semantic IDs

arXiv:2609.08310v1 Announce Type: cross Abstract: The success of generative retrieval has largely been attributed to the use of Semantic IDs, which improve over arbitrary item-level identifiers such a

Exposing Weaknesses in Emotion Recognition in Conversations

arXiv:2609.05806v1 Announce Type: new Abstract: Emotion Recognition in Conversations (ERC) aims to identify speakers' emotions in multi-turn dialogue. Accurate emotion recognition can support a wide r

False positive bias in AI-powered speech-based cognitive screening for multilingual English speakers in the UK

arXiv:2602.13047v2 Announce Type: replace Abstract: Conversational speech reveals early signs of cognitive decline, including dementia and mild cognitive impairment (MCI). AI models show promise for s

safetyarxiv-cs-cl

Tutorials

Fast Constraint Extraction for Corrective Control under STL Specifications via Logical Dependency Tracking

arXiv:2609.09439v1 Announce Type: cross Abstract: Ensuring the satisfaction of Signal Temporal Logic (STL) specifications under uncertainty is challenging, as reachability-based monitoring provides gu

tutorialsarxiv-cs-ro

FastE: Readout-Triggered Token Compression for LLM Embedding Inference

arXiv:2609.08407v1 Announce Type: new Abstract: In this study, we identify depth-dependent prefix redundancy in final-readout LLM embedding models, notably across representative backbones including Qw

Hardware

Faster than Light in Air: 8-22 tg/s Qwen3.8-Flash-Next (Q4/Q4ish) on a 32GB M4 MacBook Air

I believe I may currently hold the record for memory constrained inference for Qwen3.8–Flash-Next on Apple Silicon — needing only about 21GB of allocations. Introducing Cherenkov, an inference engine

hardwarer-localllama

FATS: A Prompt Injection Attack Utilizing Feign Security Agents with Deceptive Few-shots Learning

arXiv:2410.08776v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) face significant security risks despite their advanced capabilities. While techniques like Reinforcement Learning

Federated Learning for Cross-Modality Medical Image Segmentation via Augmentation-Driven Generalization

arXiv:2602.20773v2 Announce Type: replace Abstract: Purpose: Developing generalizable medical image segmentation models is challenging because imaging data are distributed across institutions and diff

researcharxiv-cs-cv

Feyospace-v1: How the Cyber Mercury Seven Trained Frontier Cyber Models

arXiv:2609.08418v1 Announce Type: new Abstract: Training capable cyber agents is often treated primarily as a problem of model scale, yet open-weight post-training is constrained more directly by the

Field Converter: Geometry-Initialized Temporal Residual Refinement for World-Grounded Player Pose Estimation from Soccer Broadcasts

arXiv:2609.10498v1 Announce Type: new Abstract: Recovering 3D human pose from monocular sports broadcasts remains challenging when players must be localized in a shared metric world coordinate system

researcharxiv-cs-cv

Field-level prediction of mid-plane stress tensor fields in concrete target penetration: a cross-velocity graph neural operator surrogate

model-releasesarxiv-cs-lg

arXiv:2609.10032v1 Announce Type: new Abstract: Although the impact resistance of concrete has been studied extensively, a framework linking mesoscale heterogeneity to full-field stress-tensor predict

Field-level weak lensing cosmology with 60 simulations using multifidelity simulation-based inference

arXiv:2606.23346v2 Announce Type: replace-cross Abstract: We perform a realistic KiDS-Legacy mock analysis with field-level neural compression and simulation-based inference using just 60 N-body simul

FinCUABuild: Can Agents Build Reliable Benchmarks for Dynamic Financial Computer Use?

arXiv:2609.07603v1 Announce Type: new Abstract: Financial scenarios are diverse and complex, spanning varying data conditions, tool configurations, and workflows. Yet existing CUA, Computer-Using Agen

Applications

Fine PT-PT Web: A High-Quality 41 Billion Tokens Data Collection of the European Portuguese Web

arXiv:2609.07699v2 Announce Type: cross Abstract: Curating Web corpora for regional language variants like European Portuguese (PT-PT) is heavily bottlenecked by dialectal overlap (mainly with PT-BR)

applicationsarxiv-cs-ai

Fine-Tuning a KV Cache Concatenation-Aware Model or Recomputing KV Caches? Why Not Both?

model-releasesarxiv-cs-cl

arXiv:2609.09768v1 Announce Type: cross Abstract: In Retrieval-Augmented Generation (RAG) systems, a large number of retrieved chunks are concatenated to form the input context so that users can recei

Applications

Flawed but Memorable: Student Critical Reception of Interest-Personalized GenAI Analogies in Computing Education

arXiv:2609.06095v1 Announce Type: cross Abstract: Motivation: Undergraduate computing students increasingly turn to generative AI (GenAI) tools to understand abstract concepts through analogies. Analo

applicationsarxiv-cs-ai

FlowCPO: A Unified Divergence View of Preference Alignment for Flow Models

arXiv:2609.09905v1 Announce Type: cross Abstract: Preference alignment for flow and diffusion models now spans online reinforcement learning and offline preference optimization, but the relation betwe

safetyarxiv-cs-cv

FolDeX: A Physical-World Benchmark for Long-Horizon Robotic Manipulation of Deformable Objects

model-releasesarxiv-cs-ro

arXiv:2609.10243v1 Announce Type: new Abstract: Embodied AI, including vision-language-action and world-action models, must operate reliably in the physical world. Yet methods that perform well in sim

Forgetting Only What Matters: Layer-Selective Unlearning toward Robust LLMs

model-releasesarxiv-cs-lg

arXiv:2609.10439v1 Announce Type: new Abstract: Large Language Models (LLMs) can memorize and reproduce sensitive, copyrighted, or otherwise undesirable training content, creating privacy, safety, and

Forty years ago the first worm 'escaped' containment and breached systems on the brand new Internet. It was illegal then and it's illegal no…

Forty years ago the first worm 'escaped' containment and breached systems on the brand new Internet. It was illegal then and it's illegal now. If Anthropic and OpenAI are creating worms, labeling it '

safetygary-marcus--x

Forward-Free LLM Depth Pruning via Weight Redundancy

arXiv:2609.09883v1 Announce Type: new Abstract: Depth pruning reduces large language model (LLM) inference cost by removing complete Transformer blocks. Activation-based methods collect hidden states

Local Ai

Frame-Coded Legged Locomotion over Noisy Terrain

arXiv:2609.10273v1 Announce Type: cross Abstract: Open-loop multilegged locomotion over rough terrain has been interpreted as matter transport over a noisy channel: leg-ground interactions are discret

local-aiarxiv-cs-ro