CompanyAnthropic8 recent entries9 Oct 2026AgentTime: Can Agents Estimate and Control Their Own Runtime?arXiv:2610.09944v2 Announce Type: replace Abstract: An essential control of AI agents is their ability to manage runtime. This ability requires a sense of time-awareness, to predict and estimate wall-→12 Oct 2026SWE-Duel: Towards a Self-Scaling Arena for Coding AgentsarXiv:2610.12569v1 Announce Type: new Abstract: Static coding benchmarks are fixed at release. Over time their tasks leak into training data, and stronger agents eventually saturate them. We argue tha
CompanyOpenAI8 recent entries23 Sept 2026Detecting GPT-Assisted Writing Using Interpretable Stylometric FeaturesarXiv:2609.26687v1 Announce Type: new Abstract: Distinguishing GPT-assisted from independently authored student writing has become a critical challenge in academia. This paper evaluates the discrimina→25 Sept 2026Large Language Models for Programming: Actually Fixing or Reimplementing Incorrect Code?arXiv:2609.29410v1 Announce Type: new Abstract: Recent studies have shown that Large Language Models can effectively solve problems and fix bugs in diverse programming environments, including competit→30 Sept 2026MCP Error Messages Written for Developers Hurt the Most Capable Agents MostarXiv:2609.35381v2 Announce Type: replace-cross Abstract: Many Model Context Protocol (MCP) servers wrap web APIs built for human developers, and their error messages tell the reader to run a command,→1 Oct 2026Evaluating Whether GPT-6 Astra Performs Unsanctioned Supply-Chain AttacksarXiv:2609.38415v1 Announce Type: cross Abstract: This technical report presents an alignment evaluation developed and performed by the UK AI Security Institute for assessing whether advanced AI syste→1 Oct 2026AIfred: Augmented Learning through Functional Robotic Embodiment at the DeskarXiv:2609.38737v1 Announce Type: new Abstract: Desk-based learning and creative activities benefit from handwritten engagement. However, current generative AI tools deliver guidance through a separat→2 Oct 2026A First Glance at Jev for Network Traffic Classification: Accuracy, Processing Time, and CostarXiv:2610.00376v1 Announce Type: new Abstract: We evaluate Jev on ten dataset-defined application labels in CESNET-QUICEXT-25 using only the first ten packets' sizes, directions, and inter-packet tim→7 Oct 2026JEV versus LLMs: Accuracy, Cost and Calibration on Seven Political Science ReplicationsarXiv:2610.06625v2 Announce Type: replace Abstract: Large language models (LLMs) annotate and scale political text or constructs by generating text tokens. A new class of models, which TypeSafe market→9 Oct 2026Writing for the Reviewer: Defensive Writing in GPT ModelsarXiv:2610.11355v1 Announce Type: new Abstract: Researchers increasingly use ChatGPT to revise their papers, and recent GPT versions often narrow or even retract the authors' claims. We call such chan
CompanyGoogle8 recent entries8 Oct 2026From Pixel to Coding: Evaluating the Figure Reproduction Capabilities of MLLMsarXiv:2610.10066v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in both visual understanding and code generation. However, existing b→8 Oct 2026Epistemic Policy Divergence in Multi-Turn LLM Contamination: A Protocol-Gradient InvestigationarXiv:2609.35308v2 Announce Type: replace Abstract: Large language models treat conversation history as unverified context, so false premises injected into prior turns can be adopted as fact, a failur→8 Oct 2026Do Image Editors Follow Depth-Dependent Blur and Aperture Response? A Rendered-Ground-Truth Pilot AuditarXiv:2610.09344v1 Announce Type: new Abstract: General image editors are asked to make a photo look as if it were taken at f/1.4, yet it is rarely checked whether the blur they add follows thin-lens →8 Oct 2026A Positive Case for Faithfulness: LLM Self-Explanations Help Predict Model BehaviorarXiv:2602.02639v2 Announce Type: replace-cross Abstract: LLM self-explanations are often presented as a promising tool for AI oversight, yet their faithfulness to the model's true reasoning process i→9 Oct 2026WorldBench: Evaluating LLMs on Three.js Voxel World GenerationarXiv:2610.10622v1 Announce Type: cross Abstract: Large language models can now write complete, interactive 3D worlds as code, but grading those worlds automatically is unreliable. Existing judges tak→9 Oct 2026GroundSight at GroundLM 2026 Shared Tasks: GoldenViewVQAarXiv:2610.11402v1 Announce Type: new Abstract: GoldenViewVQA requires models to jointly answer driving-scene questions and identify the camera view containing the supporting visual evidence, making p→12 Oct 2026People Making Conversation: Evaluating How Naturally Speech Models ConversearXiv:2610.12802v1 Announce Type: new Abstract: A conversational speech model has to decide, at every pause, whether to speak. Existing evaluations test boundary detection, policy adherence, or aggreg→12 Oct 2026MathVis: Can AI Reason through Drawing? A Benchmark for Mathematical Figure GenerationarXiv:2610.12954v1 Announce Type: new Abstract: This paper introduces MathVis, a benchmark designed to evaluate LLMs' ability to visualize frontier mathematical problems and perform diagram-assisted r
CompanyMeta8 recent entries8 Oct 2026Zero-shot Video Moment Retrieval via Off-the-shelf Multimodal Large Language ModelsarXiv:2501.07972v2 Announce Type: replace-cross Abstract: The target of video moment retrieval (VMR) is predicting temporal spans within a video that semantically match a given linguistic query. Exist→8 Oct 2026Tokka-Bench: Evaluating Tokenizers Across 100 Natural and 20 Programming LanguagesarXiv:2610.08794v1 Announce Type: new Abstract: Large language models rely on subword tokenizers whose quality varies across languages, yet no standardized multi-metric framework exists for broad comp→8 Oct 2026Nobody Truly Agrees on Sentiment: Humans, Bespoke Tools, and LLMs Struggle with Social Media TextsarXiv:2610.10318v1 Announce Type: new Abstract: Social media is a rich source of real-time public sentiment, but widely used sentiment analysis tools are often applied without understanding their limi→9 Oct 2026Rounding in Preconditioner Space: Redesigning 4-bit AdamW Optimizer-State QuantizationarXiv:2610.12444v1 Announce Type: new Abstract: Quantizing AdamW's optimizer states reduces persistent storage, but quantization errors propagate through the moment recurrences and perturb subsequent →12 Oct 2026Taking Tokenization off the Host for Agentic LLM ServingarXiv:2610.13325v1 Announce Type: cross Abstract: Agentic workloads resend long contexts that barely change between calls. Prefix caching now skips most of their prefill, yet every request must still →12 Oct 2026LLMail-Inject: A Dataset from a Realistic Adaptive Prompt Injection ChallengearXiv:2506.09956v2 Announce Type: replace-cross Abstract: Indirect prompt injection attacks exploit a fundamental weakness of LLMs: executing unauthorized tasks from untrusted sources. Despite the pra→12 Oct 2026Let Large Language Models Judge Each Other: Multi-Agent Peer-Reviewed Reasoning for Medical Question AnsweringarXiv:2606.15419v2 Announce Type: replace-cross Abstract: Objective: To enhance the accuracy, interpretability, and robustness of large language models (LLMs) in medical question answering (MedQA). Me→12 Oct 2026Language Models Have Interpretable Narrative MechanismsarXiv:2610.12989v1 Announce Type: new Abstract: We investigate whether language models contain internal components that contribute to representing narrativity, the degree to which a text exhibits narr
CompanyMistral8 recent entries22 Sept 2026Machine-Interpretable Information: Compiling Documents into Searchable and Readable Protocol StatesarXiv:2609.23371v1 Announce Type: new Abstract: Long-context language models interface with external knowledge through raw natural language. In retrieval-augmented systems, this creates a persistent i→22 Sept 2026Auditing Political Alignment in LLM Assistants: Engagement, Stance, and User IdentityarXiv:2609.23039v1 Announce Type: new Abstract: LLM-based AI systems answer political questions for hundreds of millions of people. Current audits measure what they say to an average user, but their b→23 Sept 2026Syndrome, Synergy, and Safety: Structured Reasoning and Knowledge-Driven Alignment for TCM Prescription GenerationarXiv:2609.25755v1 Announce Type: new Abstract: Applying large language models to Traditional Chinese Medicine (TCM) prescription generation reveals three clinically critical gaps: models produce end-→23 Sept 2026Reasoning Topology Matters: A Controlled Study of LLM-Based Cybersecurity AnalysisarXiv:2609.24710v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in cybersecurity, where accurate analysis often requires multi-step and context-dependent reasoning→23 Sept 2026Component Type, Not Reconstruction Error, Predicts Attention Quantization SensitivityarXiv:2609.26173v1 Announce Type: new Abstract: Many post-training quantization (PTQ) methods use layer-wise reconstruction, second-order proxy objectives, or activation-aware transformations to reduc→24 Sept 2026Computation Over Geometry: Meaning Identity Is Computed, Not Shipped in the EmbeddingsarXiv:2609.28290v1 Announce Type: new Abstract: Meaning identity (whether two sentences say the same thing after wording changes) is treated in retrieval and RAG as a geometric fact about independentl→29 Sept 2026From Attention Sensitivity to Layer Role: Revisiting Mixed-Precision Quantization of TransformersarXiv:2609.34866v1 Announce Type: new Abstract: Most post-training quantization pipelines fit each weight matrix to its pretrained counterpart, one matrix at a time. Whether that proxy tracks what an →2 Oct 2026Every Ablation Is a Dose: Counterweights and the Semblance of Self-RepairarXiv:2610.02173v1 Announce Type: new Abstract: Ablate a component of a language model, and other components often appear to adjust and compensate. This phenomenon, termed self-repair, has been observ
CompanyxAI8 recent entries30 Sept 2026Auditable Long-Term Memory: A Deterministic Retrieval Chain Measured at 479/475 of 500 on LongMemEval-SarXiv:2609.38021v1 Announce Type: cross Abstract: We evaluate an auditable long-term memory system on LongMemEval-S. Its retrieval chain uses hybrid candidate retrieval, cross-encoder reranking, cover→1 Oct 2026VAmoS Part Deux: Harder, More Realistic Voice-Agent SimulationarXiv:2609.38512v1 Announce Type: new Abstract: Voice agents in production must handle several requests, background speech, and customers who lose patience. We introduce VAmoS Energy, a benchmark that→1 Oct 2026DrivingBench: Can Vision-Language Models Drive a Toyota Corolla?arXiv:2609.38948v1 Announce Type: cross Abstract: Frontier models excel at many digital benchmarks, yet their ability to drive a real car, an everyday human skill, remains largely untested. We present→2 Oct 2026System Attribution in LLM Brand Recommendations: Single Responses Identify the System, Aggregated Brand Profiles Do Not TransferarXiv:2610.00253v1 Announce Type: cross Abstract: Audits of AI visibility summarise the brand recommendations of deployed language models into per-system profiles. We test whether such a profile descr→2 Oct 2026Legal text classification in Korean sexual offense cases: from traditional machine learning to large language models with XAI insightsarXiv:2610.00087v1 Announce Type: cross Abstract: The advancement of natural language processing (NLP) has expanded AI-based text classification in the legal domain. However, accurately classifying le→5 Oct 2026Dissecting model behavior through agent trajectoriesarXiv:2606.17454v3 Announce Type: replace Abstract: AI agent performance is not just a modeling problem, it is fundamentally a systems problem. The advanced capabilities of models are realized through→9 Oct 2026WorldBench: Evaluating LLMs on Three.js Voxel World GenerationarXiv:2610.10622v1 Announce Type: cross Abstract: Large language models can now write complete, interactive 3D worlds as code, but grading those worlds automatically is unreliable. Existing judges tak→12 Oct 2026People Making Conversation: Evaluating How Naturally Speech Models ConversearXiv:2610.12802v1 Announce Type: new Abstract: A conversational speech model has to decide, at every pause, whether to speak. Existing evaluations test boundary detection, policy adherence, or aggreg
CompanyDeepSeek8 recent entries9 Oct 2026A Closer Look at Agentic BBO: Benchmarking LLM Agents for Black-Box OptimizationarXiv:2610.12183v1 Announce Type: cross Abstract: Black-box optimization (BBO) arises in many scientific and engineering problems where objective evaluations are expensive and limited. Recent large la→12 Oct 2026Taking Tokenization off the Host for Agentic LLM ServingarXiv:2610.13325v1 Announce Type: cross Abstract: Agentic workloads resend long contexts that barely change between calls. Prefix caching now skips most of their prefill, yet every request must still →12 Oct 2026Not All Correct Fixes Run Alike: An Empirical Study of Patch Performance in Repository-Level Issue ResolutionarXiv:2610.13563v1 Announce Type: cross Abstract: Repository-level issue-resolution benchmarks such as SWE-bench evaluate submitted patches primarily based on whether they pass the correctness tests, →12 Oct 2026MagicBeanStalk: Escaping the RLVR Zero Advantage Trap with Recursive Data SynthesisarXiv:2610.12659v1 Announce Type: new Abstract: Data-centric scaling of RLVR has focused on collecting or generating verifiable training problems. However, corpus size alone does not determine whether→12 Oct 2026Locate, Judge, Read: Role-Specialized Tool Agents over a Knowledge-Point Index for Open-Domain Multimodal Document QAarXiv:2610.13128v1 Announce Type: new Abstract: Question answering over large heterogeneous document collections requires first finding the right passages and then reading them, yet current retrieval-→12 Oct 2026Let Large Language Models Judge Each Other: Multi-Agent Peer-Reviewed Reasoning for Medical Question AnsweringarXiv:2606.15419v2 Announce Type: replace-cross Abstract: Objective: To enhance the accuracy, interpretability, and robustness of large language models (LLMs) in medical question answering (MedQA). Me→12 Oct 2026Knowing Is Not Telling: Identity Ossification in LLM Representations of Trans-border CommunitiesarXiv:2610.12610v1 Announce Type: cross Abstract: Ethnic and cultural identities rarely align with political borders, a tension made sharper by migration and long-standing cross-border ties. Communiti→12 Oct 2026Can Language Models Play the Wikipedia Game?arXiv:2610.13948v1 Announce Type: new Abstract: Language models increasingly support or automate information retrieval from document collections too large to index. This task is difficult when it requ
CompanyNVIDIA8 recent entries11 Aug 2026Who Verifies the Benchmark? Decentralizing Trust in Large Language Model EvaluationarXiv:2608.07762v1 Announce Type: new Abstract: LLM benchmarks can build an organization's reputation and attract customers, but only when results are transparent and verifiable. Unverified claims tha→9 Sept 2026A Measurement Study of LLM Inference Trade-offs Across Edge Continuum HardwarearXiv:2609.08307v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as backends for intelligent web services, but serving them across the edge continuum requires balan→28 Sept 2026Learning to Stop without Learning to Stop: Self-Supervised Confidence Training Improves Reasoning EfficiencyarXiv:2609.31619v1 Announce Type: new Abstract: Reasoning models often generate very long reasoning traces, making inference computationally expensive. Existing approaches typically improve efficiency→29 Sept 2026Attribution Without a Second Pass: Inline Per-Sample Gradient Provenance at ~1% OverheadarXiv:2609.32380v1 Announce Type: new Abstract: Data attribution methods used in practice (TRAK, LoGRA, EK-FAC) are post-hoc: after training they make a second pass over the training set to recompute →1 Oct 2026Cycle-Aware Autoencoder with Cross-SignalConsistency for Railway Door Anomaly DetectionarXiv:2609.39035v1 Announce Type: cross Abstract: Passenger access doors are safety-critical subsystems in railway vehicles, yet detecting abnormal door behavior in real operation is challenging becau→5 Oct 2026D2K-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→6 Oct 2026Static Bootstrap Placement for Encrypted Language Model DecodingarXiv:2610.04912v1 Announce Type: cross Abstract: Language models increasingly serve prompts that carry private data, and secure inference under homomorphic encryption lets a client outsource the comp→12 Oct 2026Taking Tokenization off the Host for Agentic LLM ServingarXiv:2610.13325v1 Announce Type: cross Abstract: Agentic workloads resend long contexts that barely change between calls. Prefix caching now skips most of their prefill, yet every request must still