Every paper checked — nothing hidden

All papers

Every paper checked for brief #4 of 12–19 Oct 2026 — nothing is hidden. Brief papers first.

All17,313 In brief24 More (on-topic)3,333 Not relevant13,754 Not assessed87 Older352 Could not confirm93 Blogs with no paper266 All blogs284

Paper	Topic	Relevance
Efficient Safety Guardrails through Latent Dual-Hypothesis Reasoning with On-Demand Trace Reconstruction LR-Guard distills explicit safety reasoning into compact latent representations to speed up guardrail classification while enabling on-demand trace reconstruction. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a guardrail model rather than general benchmark evaluation.
Efficient Safety Guardrails through Latent Dual-Hypothesis Reasoning with On-Demand Trace Reconstruction LR-Guard distills explicit safety reasoning into compact latent representations to speed up guardrail classification while enabling on-demand trace reconstruction. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focus is safety guardrail inference, not planning agents.
AffectWorld: Benchmarking Emotion-Driven Future World Prediction AffectWorld benchmarks world models on predicting emotion-driven human actions and subsequent environment changes across annotated video samples. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Evaluates video world models, not tool-using language model agents.
AffectWorld: Benchmarking Emotion-Driven Future World Prediction AffectWorld benchmarks world models on predicting emotion-driven human actions and subsequent environment changes across annotated video samples. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focus is video world prediction, not safety or guardrails.
AffectWorld: Benchmarking Emotion-Driven Future World Prediction AffectWorld benchmarks world models on predicting emotion-driven human actions and subsequent environment changes across annotated video samples. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates video world models rather than language model benchmarks.
AffectWorld: Benchmarking Emotion-Driven Future World Prediction AffectWorld benchmarks world models on predicting emotion-driven human actions and subsequent environment changes across annotated video samples. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Benchmark evaluates emotion-driven world prediction, not industrial inspection.
SimInter: Evidence-Grounded User Simulation with Interview-Based Profile Enrichment for Conversational Recommender Systems SimInter simulates conversational recommendation users by conducting synthetic interviews between language models to enrich implicit behavioral profiles. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Works on text-based conversational systems, not multimodal inputs.
SimInter: Evidence-Grounded User Simulation with Interview-Based Profile Enrichment for Conversational Recommender Systems SimInter simulates conversational recommendation users by conducting synthetic interviews between language models to enrich implicit behavioral profiles. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Does not involve programming, code generation, or software engineering.
SimInter: Evidence-Grounded User Simulation with Interview-Based Profile Enrichment for Conversational Recommender Systems SimInter simulates conversational recommendation users by conducting synthetic interviews between language models to enrich implicit behavioral profiles. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on conversational recommendation simulation, not document retrieval.
SimInter: Evidence-Grounded User Simulation with Interview-Based Profile Enrichment for Conversational Recommender Systems SimInter simulates conversational recommendation users by conducting synthetic interviews between language models to enrich implicit behavioral profiles. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression, quantization, or efficient serving.
SimInter: Evidence-Grounded User Simulation with Interview-Based Profile Enrichment for Conversational Recommender Systems SimInter simulates conversational recommendation users by conducting synthetic interviews between language models to enrich implicit behavioral profiles. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a user simulation method, not an LLM benchmark.
SimInter: Evidence-Grounded User Simulation with Interview-Based Profile Enrichment for Conversational Recommender Systems SimInter simulates conversational recommendation users by conducting synthetic interviews between language models to enrich implicit behavioral profiles. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Contains an audit agent but main focus is user simulation.
Complex materials engineering via Provenance Action Graphs: potential, structure and demonstration This paper examines using provenance reaction action graphs and machine learning to encode multi-step material synthesis pathways from experimental history. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Does not evaluate language models or benchmarking methodology.
Complex materials engineering via Provenance Action Graphs: potential, structure and demonstration This paper examines using provenance reaction action graphs and machine learning to encode multi-step material synthesis pathways from experimental history. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on reaction graphs for materials science, not LLM agents.
Complex materials engineering via Provenance Action Graphs: potential, structure and demonstration This paper examines using provenance reaction action graphs and machine learning to encode multi-step material synthesis pathways from experimental history. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on materials synthesis graphs, not AI safety.
ShoeFit: A New Dataset and Dual-image-stream DiT Framework for Virtual Footwear Try-On ShoeFit presents a multi-view dataset and a dual-stream diffusion framework for consistent virtual footwear try-on synthesis. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on virtual footwear try-on, not industrial inspection.
ShoeFit: A New Dataset and Dual-image-stream DiT Framework for Virtual Footwear Try-On ShoeFit presents a multi-view dataset and a dual-stream diffusion framework for consistent virtual footwear try-on synthesis. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focus is generative image diffusion, not multimodal reasoning models.
ShoeFit: A New Dataset and Dual-image-stream DiT Framework for Virtual Footwear Try-On ShoeFit presents a multi-view dataset and a dual-stream diffusion framework for consistent virtual footwear try-on synthesis. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not focus on AI safety, alignment, or guardrails.
ShoeFit: A New Dataset and Dual-image-stream DiT Framework for Virtual Footwear Try-On ShoeFit presents a multi-view dataset and a dual-stream diffusion framework for consistent virtual footwear try-on synthesis. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Covers image generation for try-on, not LLM agents.
ShoeFit: A New Dataset and Dual-image-stream DiT Framework for Virtual Footwear Try-On ShoeFit presents a multi-view dataset and a dual-stream diffusion framework for consistent virtual footwear try-on synthesis. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Releases a dataset for footwear try-on, not LLM benchmarks.
AssetForge: Asset-Augmented Task Synthesis for Business Automation Agents AssetForge pairs reusable application assets with task rubrics to synthesize executable training tasks and evaluation checks for business automation agents. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focus is synthetic data generation for agent training, not evaluation.
AssetForge: Asset-Augmented Task Synthesis for Business Automation Agents AssetForge pairs reusable application assets with task rubrics to synthesize executable training tasks and evaluation checks for business automation agents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focus is task synthesis and training, not safety or guardrails.
Lightweight but Linguistically Fair? Quantisation Robustness across Bavarian, German, and English The authors evaluate how 8-bit and 4-bit quantization methods affect model accuracy across Bavarian, Standard German, and English benchmarks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Analyzes quantization fairness, not deployment safety or guardrails.
Lightweight but Linguistically Fair? Quantisation Robustness across Bavarian, German, and English The authors evaluate how 8-bit and 4-bit quantization methods affect model accuracy across Bavarian, Standard German, and English benchmarks. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focus is natural language dialects, not code generation.
Lightweight but Linguistically Fair? Quantisation Robustness across Bavarian, German, and English The authors evaluate how 8-bit and 4-bit quantization methods affect model accuracy across Bavarian, Standard German, and English benchmarks. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not study external document retrieval or grounding.
Lightweight but Linguistically Fair? Quantisation Robustness across Bavarian, German, and English The authors evaluate how 8-bit and 4-bit quantization methods affect model accuracy across Bavarian, Standard German, and English benchmarks. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Evaluates text-only language models, not multimodal systems.
Lightweight but Linguistically Fair? Quantisation Robustness across Bavarian, German, and English The authors evaluate how 8-bit and 4-bit quantization methods affect model accuracy across Bavarian, Standard German, and English benchmarks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Uses an existing benchmark to study quantization fairness.
Lightweight but Linguistically Fair? Quantisation Robustness across Bavarian, German, and English The authors evaluate how 8-bit and 4-bit quantization methods affect model accuracy across Bavarian, Standard German, and English benchmarks. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Paper is about model quantization, not agent planning.
Let the Mind Conceive, Let the Hand Achieve: Following Multimodal Instructions for Long-Horizon Robotic Manipulation Orchestra decomposes multimodal instructions into subtasks, tracks step progress, and maintains execution context to improve long-horizon robotic manipulation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a robotics benchmark merely to test its specific method.
Let the Mind Conceive, Let the Hand Achieve: Following Multimodal Instructions for Long-Horizon Robotic Manipulation Orchestra decomposes multimodal instructions into subtasks, tracks step progress, and maintains execution context to improve long-horizon robotic manipulation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on general robotic manipulation planning, not industrial inspection.
Let the Mind Conceive, Let the Hand Achieve: Following Multimodal Instructions for Long-Horizon Robotic Manipulation Orchestra decomposes multimodal instructions into subtasks, tracks step progress, and maintains execution context to improve long-horizon robotic manipulation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not focus on safety guardrails or deployment reliability risks.
Choosing LLMs for Strategic Agent Interactions The authors formulate a meta-game framework to dynamically route and select language models during strategic multi-agent interactions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focus is strategic decision-making benchmarks, not AI safety guardrails.
Choosing LLMs for Strategic Agent Interactions The authors formulate a meta-game framework to dynamically route and select language models during strategic multi-agent interactions. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not investigate retrieval-augmented generation systems.
ABot-ER: Unified Embodied Reasoning through Agentic Data-Model Co-Evolution The authors introduce an embodied reasoning foundation model that unifies perception, planning, and execution using task-conditioned adaptive adapters. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not address safety, guardrails, or deployment reliability risks.
ABot-ER: Unified Embodied Reasoning through Agentic Data-Model Co-Evolution The authors introduce an embodied reasoning foundation model that unifies perception, planning, and execution using task-conditioned adaptive adapters. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focus is an embodied agent architecture, not evaluating benchmarks.
Urbex: Agentic Spatial Grounding for City-Scale 3D Scenes The authors present an active spatial search framework enabling vision-language agents to ground 3D objects in city-scale environments via interactive tool use. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a grounding framework, not a general evaluation benchmark.
Urbex: Agentic Spatial Grounding for City-Scale 3D Scenes The authors present an active spatial search framework enabling vision-language agents to ground 3D objects in city-scale environments via interactive tool use. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Active 3D grounding framework, not safety or guardrails.
Urbex: Agentic Spatial Grounding for City-Scale 3D Scenes The authors present an active spatial search framework enabling vision-language agents to ground 3D objects in city-scale environments via interactive tool use. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on outdoor city-scale grounding, not industrial manufacturing inspection.
Delegating Earth science model calibration to knowledge-guided LLM agents The authors introduce a calibration harness using structured domain knowledge to safely delegate Earth science model parameterization to language model agents. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an agent workflow harness, not an LLM evaluation benchmark.
PRISM: Calibrating Black-Box Judges with Counterfactual Probes The authors propose a probe-based method to measure and calibrate systematic scoring biases in black-box LLM evaluation judges. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not focused on code LLMs or software engineering.
PRISM: Calibrating Black-Box Judges with Counterfactual Probes The authors propose a probe-based method to measure and calibrate systematic scoring biases in black-box LLM evaluation judges. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Evaluates text summarization and generation, not multimodal models.
PRISM: Calibrating Black-Box Judges with Counterfactual Probes The authors propose a probe-based method to measure and calibrate systematic scoring biases in black-box LLM evaluation judges. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on calibrating evaluation scores, not LLM agents.
PRISM: Calibrating Black-Box Judges with Counterfactual Probes The authors propose a probe-based method to measure and calibrate systematic scoring biases in black-box LLM evaluation judges. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not address retrieval-augmented generation or grounding.
PRISM: Calibrating Black-Box Judges with Counterfactual Probes The authors propose a probe-based method to measure and calibrate systematic scoring biases in black-box LLM evaluation judges. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model efficiency, pruning, or quantization.
Graph-free Retrieval with Graph-Modified Embeddings The authors evaluate offline graph-modified embeddings for vector retrieval, improving multi-hop evidence recovery without requiring query-time graph access. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on vector retrieval techniques, not autonomous multi-step agents.
Graph-free Retrieval with Graph-Modified Embeddings The authors evaluate offline graph-modified embeddings for vector retrieval, improving multi-hop evidence recovery without requiring query-time graph access. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a retrieval method, not an LLM benchmark or evaluation protocol.
Graph-free Retrieval with Graph-Modified Embeddings The authors evaluate offline graph-modified embeddings for vector retrieval, improving multi-hop evidence recovery without requiring query-time graph access. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Information retrieval paper, not AI safety or guardrails.
Not All Drift Is a Mechanism: Tracing Prompt Adaptation in LLMs The authors trace internal representations across prompt optimization techniques to show how discrete prompts, prompt tuning, and prefixes alter model processing. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies internal prompt adaptation mechanics, not autonomous agents.
Not All Drift Is a Mechanism: Tracing Prompt Adaptation in LLMs The authors trace internal representations across prompt optimization techniques to show how discrete prompts, prompt tuning, and prefixes alter model processing. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Examines text language models, not multimodal models.
Not All Drift Is a Mechanism: Tracing Prompt Adaptation in LLMs The authors trace internal representations across prompt optimization techniques to show how discrete prompts, prompt tuning, and prefixes alter model processing. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Prompt mechanics study, not deployment safety or alignment.
Not All Drift Is a Mechanism: Tracing Prompt Adaptation in LLMs The authors trace internal representations across prompt optimization techniques to show how discrete prompts, prompt tuning, and prefixes alter model processing. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Mechanistic analysis of prompts rather than model compression or serving efficiency.
Not All Drift Is a Mechanism: Tracing Prompt Adaptation in LLMs The authors trace internal representations across prompt optimization techniques to show how discrete prompts, prompt tuning, and prefixes alter model processing. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not focused on code generation or software tasks.
Not All Drift Is a Mechanism: Tracing Prompt Adaptation in LLMs The authors trace internal representations across prompt optimization techniques to show how discrete prompts, prompt tuning, and prefixes alter model processing. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not examine retrieval-augmented generation systems.
Do Factuality Methods Make LLMs More Factual? Reproducibility and Fair Comparison The authors reproduce and compare five language model factuality alignment methods on a standardized benchmark across identical base models and evaluation protocols. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not focused on software engineering or code generation.
Do Factuality Methods Make LLMs More Factual? Reproducibility and Fair Comparison The authors reproduce and compare five language model factuality alignment methods on a standardized benchmark across identical base models and evaluation protocols. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text factuality, not multimodal models.
Do Factuality Methods Make LLMs More Factual? Reproducibility and Fair Comparison The authors reproduce and compare five language model factuality alignment methods on a standardized benchmark across identical base models and evaluation protocols. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression, pruning, or efficient inference.
Do Factuality Methods Make LLMs More Factual? Reproducibility and Fair Comparison The authors reproduce and compare five language model factuality alignment methods on a standardized benchmark across identical base models and evaluation protocols. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies factuality alignment algorithms, not agent planning or tool use.
Do Factuality Methods Make LLMs More Factual? Reproducibility and Fair Comparison The authors reproduce and compare five language model factuality alignment methods on a standardized benchmark across identical base models and evaluation protocols. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Investigates parametric factuality alignment, not retrieval-augmented generation.
Resource-Efficient 3D Shape and Orientation Estimation of Road Users using Roadside RGB Camera and 3D Radar The authors propose a resource-efficient two-stage approach for estimating 3D road user shapes and orientations from roadside cameras and radar. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Autonomous driving perception, not AI deployment safety or alignment.
Resource-Efficient 3D Shape and Orientation Estimation of Road Users using Roadside RGB Camera and 3D Radar The authors propose a resource-efficient two-stage approach for estimating 3D road user shapes and orientations from roadside cameras and radar. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Roadside traffic perception system, not an LLM agent.

13,981–14,040 of 17,313← Previous Next →