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
MotionCoach: Constructing Multimodal Guidance for Video Professional Motion Editing Proposes a multimodal agent providing grounded prompts and video clips to guide video editing models in professional sports motions. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces VPME for evaluation, but core contribution is a video motion editing method.
MotionCoach: Constructing Multimodal Guidance for Video Professional Motion Editing Proposes a multimodal agent providing grounded prompts and video clips to guide video editing models in professional sports motions. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Uses an agent workflow as a tool for video editing rather than studying autonomous agents.
SmartSalesBench: A Resistance-Aware Multi-Turn Benchmark for Evaluating LLM Sales Agents Introduces a multi-turn conversational benchmark to evaluate sales agents against resistant, psychologically grounded buyer personas. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Uses RAG internally for dialogue grounding but does not contribute to RAG methods.
SmartSalesBench: A Resistance-Aware Multi-Turn Benchmark for Evaluating LLM Sales Agents Introduces a multi-turn conversational benchmark to evaluate sales agents against resistant, psychologically grounded buyer personas. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Evaluates text-based multi-turn conversational agents without multimodal modalities.
SmartSalesBench: A Resistance-Aware Multi-Turn Benchmark for Evaluating LLM Sales Agents Introduces a multi-turn conversational benchmark to evaluate sales agents against resistant, psychologically grounded buyer personas. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Evaluates commercial sales performance and persistence rather than safety or guardrails.
SmartSalesBench: A Resistance-Aware Multi-Turn Benchmark for Evaluating LLM Sales Agents Introduces a multi-turn conversational benchmark to evaluate sales agents against resistant, psychologically grounded buyer personas. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Evaluates sales dialogue agents, not models that write, review, or fix code.
SmartSalesBench: A Resistance-Aware Multi-Turn Benchmark for Evaluating LLM Sales Agents Introduces a multi-turn conversational benchmark to evaluate sales agents against resistant, psychologically grounded buyer personas. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Benchmark paper with no focus on model quantization, distillation, or efficient serving.
Beyond Semantic IDs: Finite-Beam Reachability and Continuous-Query Generation in Generative Recommendation Proposes continuous-query generation paradigms to eliminate reachability constraints caused by finite-beam decoding over discrete semantic IDs in recommendation. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents a continuous-query recommendation architecture rather than LLM evaluation benchmarks.
Beyond Semantic IDs: Finite-Beam Reachability and Continuous-Query Generation in Generative Recommendation Proposes continuous-query generation paradigms to eliminate reachability constraints caused by finite-beam decoding over discrete semantic IDs in recommendation. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Concerns recommendation system retrieval architecture, not LLM agents taking environment actions.
Beyond Semantic IDs: Finite-Beam Reachability and Continuous-Query Generation in Generative Recommendation Proposes continuous-query generation paradigms to eliminate reachability constraints caused by finite-beam decoding over discrete semantic IDs in recommendation. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on recommender retrieval reachability, not AI safety or deployment guardrails.
The Evidence Anchoring Boundary: Where Visual Interventions Matter and When They Help in Multimodal LLMs Analyzes layer-wise visual interventions in multimodal language models to identify where visual edits effectively reduce hallucinations. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Investigates intervention mechanics and hallucination reduction rather than proposing a benchmark.
The Evidence Anchoring Boundary: Where Visual Interventions Matter and When They Help in Multimodal LLMs Analyzes layer-wise visual interventions in multimodal language models to identify where visual edits effectively reduce hallucinations. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Investigates internal representation interventions in vision-language decoders, not agent planning.
The Evidence Anchoring Boundary: Where Visual Interventions Matter and When They Help in Multimodal LLMs Analyzes layer-wise visual interventions in multimodal language models to identify where visual edits effectively reduce hallucinations. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on general multimodal LLM hallucination rather than industrial computer vision inspection.
Work2Skill: Capturing Human GUI Experience for Reusable Agent Skill Induction Introduces a framework and multimodal dataset of human desktop demonstrations to induce reusable skills for computer-use agents. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Primary focus is skill induction for agents, though it includes an evaluation dataset.
Work2Skill: Capturing Human GUI Experience for Reusable Agent Skill Induction Introduces a framework and multimodal dataset of human desktop demonstrations to induce reusable skills for computer-use agents. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses GUI task automation rather than deployment safety, jailbreaks, or guardrails.
What Should SFT Learn? Conditional Information Gain for Generalizable Post-Training Introduces an information-theoretic framework to reweight supervised fine-tuning data based on conditional information gain. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Pertains strictly to language model post-training without multimodal components.
What Should SFT Learn? Conditional Information Gain for Generalizable Post-Training Introduces an information-theoretic framework to reweight supervised fine-tuning data based on conditional information gain. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses generalization during SFT rather than safety, hallucination mitigation, or robustness.
What Should SFT Learn? Conditional Information Gain for Generalizable Post-Training Introduces an information-theoretic framework to reweight supervised fine-tuning data based on conditional information gain. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes a training method rather than an evaluation benchmark or judge metric.
What Should SFT Learn? Conditional Information Gain for Generalizable Post-Training Introduces an information-theoretic framework to reweight supervised fine-tuning data based on conditional information gain. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on SFT token weighting rather than retrieval or grounded knowledge bases.
What Should SFT Learn? Conditional Information Gain for Generalizable Post-Training Introduces an information-theoretic framework to reweight supervised fine-tuning data based on conditional information gain. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Addresses general supervised fine-tuning techniques, not agent planning or tool use.
What Should SFT Learn? Conditional Information Gain for Generalizable Post-Training Introduces an information-theoretic framework to reweight supervised fine-tuning data based on conditional information gain. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant General LLM post-training framework not specifically focused on software engineering or code.
Benchmarking Embedding Geometries and Clustering Algorithms for Unsupervised Patent Claim Classification Compares hyperbolic and Euclidean embeddings for unsupervised patent representation to improve cross-category prior-art discovery. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Operates exclusively on patent text without multimodal image or audio modalities.
Benchmarking Embedding Geometries and Clustering Algorithms for Unsupervised Patent Claim Classification Compares hyperbolic and Euclidean embeddings for unsupervised patent representation to improve cross-category prior-art discovery. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Study of embedding geometry and clustering for patent search, not multi-step tool-using agents.
Benchmarking Embedding Geometries and Clustering Algorithms for Unsupervised Patent Claim Classification Compares hyperbolic and Euclidean embeddings for unsupervised patent representation to improve cross-category prior-art discovery. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not address safety, jailbreaking, red-teaming, or model guardrails.
Benchmarking Embedding Geometries and Clustering Algorithms for Unsupervised Patent Claim Classification Compares hyperbolic and Euclidean embeddings for unsupervised patent representation to improve cross-category prior-art discovery. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Applies to patent claims rather than software code or programming assistants.
Benchmarking Embedding Geometries and Clustering Algorithms for Unsupervised Patent Claim Classification Compares hyperbolic and Euclidean embeddings for unsupervised patent representation to improve cross-category prior-art discovery. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Examines representation geometry rather than model compression, quantization, or efficient serving.
Benchmarking Embedding Geometries and Clustering Algorithms for Unsupervised Patent Claim Classification Compares hyperbolic and Euclidean embeddings for unsupervised patent representation to improve cross-category prior-art discovery. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Evaluates clustering representations, not language model benchmarking or LLM-as-a-judge.
Hindsight Structured Advice Distillation: Steering Executor Agents with Advisors Trained on Their Failures and Task Characteristics Trains an advisor model on past failures to add dynamic, structured guidance to frozen language model agent prompts. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Deals with domain-specific agent guidance rather than safety, guardrails, or alignment.
Hindsight Structured Advice Distillation: Steering Executor Agents with Advisors Trained on Their Failures and Task Characteristics Trains an advisor model on past failures to add dynamic, structured guidance to frozen language model agent prompts. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Focuses on an agent advisor training framework rather than LLM benchmarking or evaluation methodology.
Scaling Empowered Agents Presents a scalable method to maximize agent empowerment by guiding agents toward states affording numerous executable skills. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Explores unsupervised RL objectives rather than LLM benchmarks or evaluation methodologies.
Scaling Empowered Agents Presents a scalable method to maximize agent empowerment by guiding agents toward states affording numerous executable skills. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Investigates reinforcement learning empowerment scaling, not model compression or efficient serving.
Scaling Empowered Agents Presents a scalable method to maximize agent empowerment by guiding agents toward states affording numerous executable skills. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on intrinsic motivation in RL rather than AI safety, red-teaming, or guardrails.
Scaling Empowered Agents Presents a scalable method to maximize agent empowerment by guiding agents toward states affording numerous executable skills. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Studies RL empowerment and skill learning in environments like Crafter, not LLM agent systems.
Provenance-Aware Belief Updating in Human-AI Feedback Loops Introduces a training objective enabling language models to update beliefs using report lineage rather than raw citation repetition. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Investigates Bayesian belief updates from relayed inputs, not multi-step autonomous agents.
Provenance-Aware Belief Updating in Human-AI Feedback Loops Introduces a training objective enabling language models to update beliefs using report lineage rather than raw citation repetition. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes a training method for belief updating rather than a comprehensive evaluation benchmark.
Generalized Proximal Policy Optimization: Unifying LLM RL Algorithms from the Perspective of Divergence Proposes a generalized f-divergence trust-region framework to unify and stabilize reinforcement learning algorithms for LLM post-training. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Presents a theoretical RL optimization framework, not autonomous multi-step agents or tool use.
Generalized Proximal Policy Optimization: Unifying LLM RL Algorithms from the Perspective of Divergence Proposes a generalized f-divergence trust-region framework to unify and stabilize reinforcement learning algorithms for LLM post-training. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Addresses RL algorithmic formulation rather than model compression, quantization, or serving.
Generalized Proximal Policy Optimization: Unifying LLM RL Algorithms from the Perspective of Divergence Proposes a generalized f-divergence trust-region framework to unify and stabilize reinforcement learning algorithms for LLM post-training. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Focuses on reinforcement learning policy optimization rather than benchmark design or evaluation.
Generalized Proximal Policy Optimization: Unifying LLM RL Algorithms from the Perspective of Divergence Proposes a generalized f-divergence trust-region framework to unify and stabilize reinforcement learning algorithms for LLM post-training. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on general RL mathematical theory and convergence rather than safety alignment or guardrails.
EmbodiedCLBench: Evaluating Continual Learning for Self-Evolving Embodied Agents Presents a simulation benchmark evaluating how embodied agents continually acquire basic skills and recombine them for complex tasks. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on skill generalization in embodied settings rather than AI safety or reliability.
EmbodiedCLBench: Evaluating Continual Learning for Self-Evolving Embodied Agents Presents a simulation benchmark evaluating how embodied agents continually acquire basic skills and recombine them for complex tasks. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on embodied AI simulation rather than industrial visual inspection or manufacturing.
EmbodiedCLBench: Evaluating Continual Learning for Self-Evolving Embodied Agents Presents a simulation benchmark evaluating how embodied agents continually acquire basic skills and recombine them for complex tasks. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Addresses continual learning benchmarks rather than model compression or efficient inference.
EmbodiedCLBench: Evaluating Continual Learning for Self-Evolving Embodied Agents Presents a simulation benchmark evaluating how embodied agents continually acquire basic skills and recombine them for complex tasks. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Evaluates embodied task execution in simulation, not software engineering or code generation.
EmbodiedCLBench: Evaluating Continual Learning for Self-Evolving Embodied Agents Presents a simulation benchmark evaluating how embodied agents continually acquire basic skills and recombine them for complex tasks. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on continual learning benchmark design rather than multimodal modeling architectures.
EmbodiedCLBench: Evaluating Continual Learning for Self-Evolving Embodied Agents Presents a simulation benchmark evaluating how embodied agents continually acquire basic skills and recombine them for complex tasks. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on embodied skill acquisition and transfer, not document retrieval or RAG pipelines.
Don't Guess, Just Ask: Evaluating Interactive Requirement Clarification for Repository-Level Software Development Introduces a benchmark to evaluate how effectively LLMs clarify ambiguous user requirements in repository-level software development tasks. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses requirement ambiguity in software engineering rather than safety, alignment, or guardrails.
Don't Guess, Just Ask: Evaluating Interactive Requirement Clarification for Repository-Level Software Development Introduces a benchmark to evaluate how effectively LLMs clarify ambiguous user requirements in repository-level software development tasks. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on requirement clarification benchmarks rather than multi-step autonomous agent architectures.
GeoGR: Spatial-Semantic Token Compression and Group Routing for Efficient 3D VLMs Proposes spatial-semantic token compression and layer routing to accelerate 3D vision-language model inference across multi-view inputs. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on computational efficiency rather than model safety, reliability, or guardrails.
GeoGR: Spatial-Semantic Token Compression and Group Routing for Efficient 3D VLMs Proposes spatial-semantic token compression and layer routing to accelerate 3D vision-language model inference across multi-view inputs. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Addresses 3D vision-language representation compression, not autonomous multi-step agents.
GeoGR: Spatial-Semantic Token Compression and Group Routing for Efficient 3D VLMs Proposes spatial-semantic token compression and layer routing to accelerate 3D vision-language model inference across multi-view inputs. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on general 3D VLM inference rather than industrial inspection or manufacturing perception.
GeoGR: Spatial-Semantic Token Compression and Group Routing for Efficient 3D VLMs Proposes spatial-semantic token compression and layer routing to accelerate 3D vision-language model inference across multi-view inputs. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on 3D token compression during inference, not document retrieval or grounding.
GeoGR: Spatial-Semantic Token Compression and Group Routing for Efficient 3D VLMs Proposes spatial-semantic token compression and layer routing to accelerate 3D vision-language model inference across multi-view inputs. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Does not involve software development, code generation, or program analysis.
GeoGR: Spatial-Semantic Token Compression and Group Routing for Efficient 3D VLMs Proposes spatial-semantic token compression and layer routing to accelerate 3D vision-language model inference across multi-view inputs. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes an architectural efficiency framework rather than an evaluation benchmark.
CoBe: A Benchmark For Conversational Counterfactual Text Editing Presents a conversational benchmark testing whether models can edit text under counterfactual changes while preserving unaffected factual details. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not address model compression, distillation, quantization, or inference acceleration.
CoBe: A Benchmark For Conversational Counterfactual Text Editing Presents a conversational benchmark testing whether models can edit text under counterfactual changes while preserving unaffected factual details. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Assesses counterfactual text rewriting models, not multi-step autonomous agents or tool use.
CoBe: A Benchmark For Conversational Counterfactual Text Editing Presents a conversational benchmark testing whether models can edit text under counterfactual changes while preserving unaffected factual details. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses entirely on text editing and natural language counterfactuals, with no multimodal aspects.
CoBe: A Benchmark For Conversational Counterfactual Text Editing Presents a conversational benchmark testing whether models can edit text under counterfactual changes while preserving unaffected factual details. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on conversational natural language editing rather than code generation or repair.
CoBe: A Benchmark For Conversational Counterfactual Text Editing Presents a conversational benchmark testing whether models can edit text under counterfactual changes while preserving unaffected factual details. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Evaluates causal text modification rather than safety guardrails, jailbreaks, or deployment risks.
CoBe: A Benchmark For Conversational Counterfactual Text Editing Presents a conversational benchmark testing whether models can edit text under counterfactual changes while preserving unaffected factual details. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Addresses counterfactual causal reasoning in text, not document retrieval or knowledge grounding.
Travel Back To Move Forward: Retrospective Exploration and Evolving for Long Horizon Agents Introduces a framework allowing agents to rewind long-horizon environments to earlier states while retaining learned exploratory knowledge. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Evaluates on interactive environments like Sokoban and ALFWorld, not software engineering or coding.

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