Search: “gpt”

If you maintain a skill library for long-horizon agents, this one is worth your time. (bookmark it) It discusses one of most common topics I…

If you maintain a skill library for long-horizon agents, this one is worth your time. (bookmark it) It discusses one of most common topics I get asked about these days. It shares some good ideas on ho

model-releasesdair-ai--x

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JIT-Agent: Scaling Harness Intelligence via Just-in-Time Harness Evolution

DGX agent

arXiv:2608.25593v1 Announce Type: new Abstract: Agent capability is not determined by the model alone. The agent harness, encompassing memory management, planning strategy, action protocol, and tool/s

model-releasesarxiv-cs-cl

27 Aug 2026

Neat paper suggesting that human augmentation and task automation are not necessarily related. Models that are really good at doing work are…

model-releasesethan-mollick--x

Neat paper suggesting that human augmentation and task automation are not necessarily related. Models that are really good at doing work are not always good at helping humans do work better. Given the

OpenSanctions Pairs: Large-Scale Entity Matching with LLMs

model-releasesarxiv-cs-cl

arXiv:2603.11051v2 Announce Type: replace-cross Abstract: We release OpenSanctions Pairs, the first large-scale public benchmark for entity matching on sanctions and OSINT data. The dataset includes 7

The Imperfective Paradox Is Not Necessarily in Large Language Models: A Benchmark Failure Before a Model Failure

model-releasesarxiv-cs-cl

arXiv:2608.25005v1 Announce Type: new Abstract: The imperfective paradox provides a useful test of compositional semantic analysis. Recent work constructs an NLI benchmark and reports that models freq

Visual General Intelligence: A White Paper

arXiv:2608.25924v1 Announce Type: new Abstract: This paper reconsiders intelligence from a vision-centered perspective and examines whether intelligence emerging from visual experience and learning ma

researcharxiv-cs-cv

ADVERSA: Measuring Multi-Turn Guardrail Degradation and Judge Reliability in Large Language Models

arXiv:2603.10068v2 Announce Type: replace-cross Abstract: Most adversarial evaluations of large language model (LLM) safety assess single prompts and report binary pass/fail outcomes, which fails to c

Can large language models assist choice modelling? Insights into prompting strategies and current models' capabilities

arXiv:2507.21790v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are becoming widely used to support various workflows across different disciplines, yet their potential in discre

'Ghaib in Translation' aka Unseen Harm: Measuring Cross-Script Safety Inconsistency with 'Missed-in-Urdu' Scores in LLM Hate Speech Detection

arXiv:2608.24191v1 Announce Type: cross Abstract: Urdu, the world's tenth most spoken language with 246 million speakers, remains almost entirely absent from mainstream LLM safety evaluation and nine

Great new paper from AWS on agent handoff tax. If you build agents today, you need to understand the so-called handoff tax. (bookmark it) Es…

Great new paper from AWS on agent handoff tax. If you build agents today, you need to understand the so-called handoff tax. (bookmark it) Escalating to a stronger model mid-run is usually the resort w

model-releasesdair-ai--x

Neurosymbolic Alignment for Physiologically-Safe Clinical Language Models

arXiv:2608.24534v1 Announce Type: new Abstract: Clinical LLMs can generate recommendations that are factually plausible yet physiologically unsafe. We investigate whether safety alignment can be impro

Recursive Experiential-Working Memory Evolution for Long-Horizon Agent Harnesses

arXiv:2608.24876v1 Announce Type: new Abstract: Recursive self-improvement (RSI) remains hard in long-horizon tasks, where growing histories obscure the task state and misalign skill invocation. We in

REFINE: A Multi-Agent LLM Approach for Evidence-Guided Code Refactoring

arXiv:2608.23611v1 Announce Type: cross Abstract: Large Language Models (LLMs) offer new opportunities for automated code refactoring. However, generated changes must reduce targeted quality problems

StarHarness: Evolving Harnesses with Stratified Search for Enterprise Environments

arXiv:2608.24804v1 Announce Type: new Abstract: We present StarHarness, a framework for evolving environment-specific agent harnesses while keeping model weights fixed. The evolved harness can include

StepGuard: Learning Step-Level Guardrails with Scalable Supervision and Safety-Utility Balancing

arXiv:2608.24777v1 Announce Type: new Abstract: LLM-based agents can interact with external environments through tool invocation, but this capability also introduces security risks such as file modifi

Structured Frequency-Domain Evidence for LLM-Based Time-Series Anomaly Detection

arXiv:2608.24113v1 Announce Type: cross Abstract: Time-series anomalies can appear not only as pointwise deviations but also as changes in recurring temporal structure, such as shifted periodicity or

The Handoff Tax: Continuing Non-Native Trajectories in LLM Agents

arXiv:2608.24358v1 Announce Type: new Abstract: Coding agents perform long-running tasks spanning dozens of model calls, tool uses, and code edits. As these runs unfold, users face a practical cost-qu

AraDetox: A Multi-Dialect Arabic Detoxification Dataset

arXiv:2608.22894v1 Announce Type: cross Abstract: Arabic harmful-language detection has received considerable attention, yet Arabic text detoxification remains underexplored. We introduce AraDetox, a

Beyond Dense Adam States: Adaptive Log-Space Quantization for Memory-Efficient Optimizers

model-releasesarxiv-cs-lg

arXiv:2608.22322v1 Announce Type: new Abstract: Low-precision optimizer-state methods are commonly designed for dense Adam-style moments, but memory-efficient optimizers maintain factored, confidence-

BioMed-Agent-RL: A Meta Learning, All You Need for Biomedical Applications

arXiv:2608.21864v1 Announce Type: cross Abstract: The current progress of Clinical Vision Large Language Models (C-VLLMs) has substantially improved digital diagnostics, still these frameworks often e

CatchBench: When Can an Agent Failure Be Caught?

model-releasesarxiv-cs-lg

arXiv:2608.22808v1 Announce Type: new Abstract: When can an agent failure be caught? An audit is usually limited by the record rather than by the method. CatchBench therefore puts one auditor's questi

CONTRAMEM: Learning Self-Evolving Procedural Memory from Contrasting Multi-Model Trajectories

arXiv:2608.22533v1 Announce Type: new Abstract: Autonomous computer-use agents are increasingly applied to long-horizon tasks requiring coordinated application calls, persistent state tracking, and ve

Disagree to Explore, Agree to Commit: Routing-Guided Test-Time Scaling for Software Agents

arXiv:2608.22191v1 Announce Type: new Abstract: Software-engineering agents solve repository-level tasks through long, stochastic tool-use trajectories, and repeated attempts often find fixes missed b

Dual-Grained Agent Memory and Shapley Context Attribution for Multimodal Agentic Learner

model-releasesarxiv-cs-cv

arXiv:2608.23268v1 Announce Type: new Abstract: Frontier multimodal large language models (MLLMs) deliver impressive perception yet still falter on scientific and mathematical reasoning. Parameter-lev

FigmaTrace: Capturing Creative Nuances in Human Figma Design Workflows

arXiv:2608.21460v1 Announce Type: cross Abstract: Vision Language Models have recently shown improvements in several objective and verifiable domains such as object detection but continue to underperf

From Inertia to Objectivity: Improving Deep Research Agents with Noise Isolation

arXiv:2608.23045v1 Announce Type: new Abstract: Web search agents powered by Large Language Models (LLMs) show strong promise, but deep research tasks expose a recurring failure mode: once an agent ha

From Mastery Profile to Simulated Response: Stochastic Student Knowledge Graphs (SSKG) for Faithful LLM Student Simulation

arXiv:2608.21668v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to simulate students at different mastery levels. These simulations can generate synthetic training d

Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems

arXiv:2606.17443v2 Announce Type: replace Abstract: Large language models (LLMs) are becoming a major way for consumers to find products, but we do not yet understand how brands compete in this new ch

K-Bench: measuring model performance on real scientific agent requests

arXiv:2608.21601v1 Announce Type: new Abstract: Benchmarks for scientific artificial intelligence are mostly written to be scored: multiple-choice questions, curated agent tasks with reference solutio

Safety

MCP-Universe RL: A Framework for Training MCP Tool-Use Agents via Reinforcement Learning

arXiv:2608.22167v1 Announce Type: new Abstract: Reinforcement learning (RL) has become an effective way to improve the tool-use ability of large language models (LLMs), but most existing RL frameworks

safetyarxiv-cs-ai

MEMORY Wins All: Indirect Bias Injection Attacks via Social Media Feeds

arXiv:2608.22061v1 Announce Type: new Abstract: Personal AI agents routinely consume external content while performing tasks such as web browsing, email processing, and SNS feed summarization, and the

Meta-Ctrl: Guaranteed Plan Generation by Decoupling Syntactic and Semantic Constraints

arXiv:2608.22149v1 Announce Type: cross Abstract: LLMs generate fluent plans for robots but routinely violate the syntactic and se8mantic constraints they must satisfy to execute, and existing remedie

Motion-Aware Reasoning from Speech to Mask Tracks: Runner-up Solution for the MeViS-Audio Track of the 8th LSVOS Challenge 2026

arXiv:2608.22337v1 Announce Type: cross Abstract: Speech-guided referring video object segmentation aims to recover the mask tracks of objects specified by a spoken motion description. Here, speech ca

researcharxiv-cs-cv

On Predicting Vulnerability Severity Using In-Context Learning: An Industrial Case Study

arXiv:2608.22089v1 Announce Type: cross Abstract: Modern software systems require earlier and more scalable vulnerability severity assessment to reduce exposure to high-impact security flaws. Security

Sigmoid Attention as a Better Substrate for Learned KV Cache Eviction

arXiv:2608.23296v1 Announce Type: cross Abstract: Learned KV-cache eviction often faces a soft-to-hard mismatch: during training, differentiable gates typically attenuate token contributions, whereas

researcharxiv-cs-ai

Training Proactive and Personalized LLM Agents

arXiv:2511.02208v2 Announce Type: replace Abstract: Despite rapid progress, current AI agents are primarily optimized for isolated task completion. We argue for a paradigm shift toward training agents

Unveiling the Depth-Performance Dilemma in Split-Federated Fine-tuning of LLMs

model-releasesarxiv-cs-cl

arXiv:2608.22188v1 Announce Type: cross Abstract: Split Federated Fine-tuning (SFF) is a promising paradigm for scaling Large Language Models (LLMs) by partitioning model depth between resource-constr

v0.3.0

model-releasesllama-cpp-releases

Overview llama.cpp 0.3.0 introduces the dots3-note multimodal model (with a new DSA-ISWA KV cache), MTP support for GLM-4.5-Air, and tensor-split (-sm tensor) plus multi-sequence rollback fixes for De

XTC: Head-Aware Sampling by Excluding Top Choices

arXiv:2608.22758v1 Announce Type: cross Abstract: Standard decoding rules for autoregressive language models promote diversity by rescaling the full next-token distribution or truncating its low-proba

Safety

AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning

arXiv:2508.14313v4 Announce Type: replace-cross Abstract: Test-time scaling strategies for Large Language Models predominantly rely on either reinforcement learning with sparse outcome rewards or sear

safetyarxiv-cs-ai

Annotations as Rollouts: Efficient and Scalable Reinforcement Learning for Video MLLMs

model-releasesarxiv-cs-cv

arXiv:2608.20492v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have become a prevailing paradigm for unified video perception. However, post-training on large multi-task data

ARQ: Agentic CodeQL Query Refinement for C/C++ Vulnerability Detection

arXiv:2608.20637v1 Announce Type: cross Abstract: Static analyzers have been widely adopted for vulnerability detection in C/C++ programs. Query-based static analyzers (e.g., CodeQL) encode vulnerable

Can Legal AI Know When It Is Wrong? And Do Students Know When It Is?

arXiv:2608.21089v1 Announce Type: new Abstract: Integrating Large Language Models (LLMs) into the Indian judiciary promises access to justice but introduces severe risks. We identify the 'inertia of c

Clarify-Then-Search: A Clarification Benchmark for Deep Search with End-to-End Nugget Restoration

arXiv:2608.20357v1 Announce Type: cross Abstract: Deep search is brittle on underspecified user queries: missing constraints such as time, location, scope, or definitions can lead to retrieval drift a

Designing a Robust LLM-Based Evaluation System for Agentic AI in Drug Discovery Through Human Alignment

model-releasesarxiv-cs-lg

arXiv:2608.21057v1 Announce Type: new Abstract: Agentic large language model (LLM) systems are reshaping scientific workflows in chemistry and drug discovery, but evaluating their open-ended, tool-aug

Free-Text Evaluation of LLMs for 5G Domain Knowledge and Fault Analysis using LLM-as-Judge

arXiv:2608.21021v1 Announce Type: cross Abstract: Real-world fault analysis in 5G and emerging 6G networks demands domain expertise to analyze free-text diagnostics, including root-cause explanations

Large Scale AI Grading of Handwritten Physics Assessments: Score Agreement and Olympiad Team Selection Outcomes

arXiv:2608.20521v1 Announce Type: cross Abstract: Multimodal AI can read handwritten physics solutions, but high-stakes grading requires agreement with official scores and outcomes. This study evaluat

Poly-InstructTTS: Learning In-the-Wild Expressive Speech Synthesis from Open-Ended Instructions

arXiv:2608.20387v1 Announce Type: cross Abstract: While recent text-to-speech (TTS) models achieve high naturalness, controlling fine-grained expression via natural-language instructions remains chall

researcharxiv-cs-ai