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
Seeing, Maintaining, and Learning: The Evolution of Multimodal Agent Memory The paper provides an analytical framework for how multimodal agents form, represent, and adapt memory over long-horizon tasks. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Analyzes agent cognitive memory, not document retrieval systems.
Seeing, Maintaining, and Learning: The Evolution of Multimodal Agent Memory The paper provides an analytical framework for how multimodal agents form, represent, and adapt memory over long-horizon tasks. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on multimodal agent memory, not code generation or repair.
Beyond Corrected Actions: Training LLM Agents on Textual Critiques The paper proposes a fine-tuning method that trains language model agents using textual expert critiques alongside corrected actions. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on critique-based fine-tuning rather than model pruning or quantization.
Beyond Corrected Actions: Training LLM Agents on Textual Critiques The paper proposes a fine-tuning method that trains language model agents using textual expert critiques alongside corrected actions. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Focuses on multi-turn action training, not retrieval-augmented generation.
Beyond Corrected Actions: Training LLM Agents on Textual Critiques The paper proposes a fine-tuning method that trains language model agents using textual expert critiques alongside corrected actions. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Evaluates web and retail agents, not software engineering or code LLMs.
Beyond Corrected Actions: Training LLM Agents on Textual Critiques The paper proposes a fine-tuning method that trains language model agents using textual expert critiques alongside corrected actions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Targets general agent task performance rather than safety guardrails or robustness.
Beyond Corrected Actions: Training LLM Agents on Textual Critiques The paper proposes a fine-tuning method that trains language model agents using textual expert critiques alongside corrected actions. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Applies strictly to text-based multi-turn agent environments.
Beyond Corrected Actions: Training LLM Agents on Textual Critiques The paper proposes a fine-tuning method that trains language model agents using textual expert critiques alongside corrected actions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces a fine-tuning methodology rather than an evaluation benchmark.
Policy-Verified Self-Training: Learning Agent Compliance from Verifiable Policy Rewards The paper introduces a self-training method that uses runtime policy verification feedback as rewards to teach agents safety compliance. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on policy compliance and preference training, not efficiency or quantization.
Policy-Verified Self-Training: Learning Agent Compliance from Verifiable Policy Rewards The paper introduces a self-training method that uses runtime policy verification feedback as rewards to teach agents safety compliance. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes a training framework rather than an LLM evaluation benchmark.
Evaluating Compressed Memory: Dependence, Utility, and Content Benefit The paper evaluates whether language models genuinely benefit from compressed memory by comparing them against memory-free baseline models. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Studies passage memory utility in QA, not code generation.
Evaluating Compressed Memory: Dependence, Utility, and Content Benefit The paper evaluates whether language models genuinely benefit from compressed memory by comparing them against memory-free baseline models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on evaluation methodology rather than deployment safety or jailbreaks.
Evaluating Compressed Memory: Dependence, Utility, and Content Benefit The paper evaluates whether language models genuinely benefit from compressed memory by comparing them against memory-free baseline models. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies memory grounding methodology rather than model compression techniques.
Evaluating Compressed Memory: Dependence, Utility, and Content Benefit The paper evaluates whether language models genuinely benefit from compressed memory by comparing them against memory-free baseline models. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses exclusively on text passages and memory representations.
Evaluating Compressed Memory: Dependence, Utility, and Content Benefit The paper evaluates whether language models genuinely benefit from compressed memory by comparing them against memory-free baseline models. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Investigates memory representations in QA/story generation, not agent tool use.
DeLoop: Repeated Actions as Self-Supervision for Long-Horizon Agents The paper proposes a self-supervised method that uses repetitive tool calls as negative signals to reduce looping behavior in long-horizon agents. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces a preference optimization training method, not an evaluation benchmark.
EvoWeave: Composing Reusable Agent Experience through Operation Level Graphs The paper introduces an operation-level graph framework that helps interactive agents retrieve and compose reusable fragments from past task experience. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on workflow decomposition and reuse rather than safety or reliability.
EvoWeave: Composing Reusable Agent Experience through Operation Level Graphs The paper introduces an operation-level graph framework that helps interactive agents retrieve and compose reusable fragments from past task experience. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes an agent memory mechanism rather than a standard benchmark.
ixi-sLM: On-Device sLM for AI Call Agent Service The paper presents techniques to adapt and quantize a small language model for on-device deployment in phone call agent services. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on on-device model compression, not safety, guardrails, or hallucination mitigation.
ixi-sLM: On-Device sLM for AI Call Agent Service The paper presents techniques to adapt and quantize a small language model for on-device deployment in phone call agent services. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Engineering report on model deployment, not an evaluation benchmark.
STRATA: Self-Maintaining Structured Experience Graphs for Self-Evolving Language Agents The paper introduces a structured experience graph memory that organizes and maintains agent workflows across hierarchical abstraction levels. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes an agent memory architecture, not an evaluation framework or benchmark.
STRATA: Self-Maintaining Structured Experience Graphs for Self-Evolving Language Agents The paper introduces a structured experience graph memory that organizes and maintains agent workflows across hierarchical abstraction levels. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on agent memory retention and organization rather than AI safety.
ELARSpeech: A 30K-Hour Dataset Across 675 Underrepresented Languages for Long-Tail Multilingual Speech Technologies The paper introduces a speech dataset spanning 675 underrepresented languages to support speech recognition and text-to-speech technologies. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on linguistic coverage, not model compression or serving efficiency.
ELARSpeech: A 30K-Hour Dataset Across 675 Underrepresented Languages for Long-Tail Multilingual Speech Technologies The paper introduces a speech dataset spanning 675 underrepresented languages to support speech recognition and text-to-speech technologies. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Speech recognition dataset, not safety guardrails or jailbreak prevention.
ELARSpeech: A 30K-Hour Dataset Across 675 Underrepresented Languages for Long-Tail Multilingual Speech Technologies The paper introduces a speech dataset spanning 675 underrepresented languages to support speech recognition and text-to-speech technologies. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Acoustic speech corpus, not retrieval-augmented generation system.
ELARSpeech: A 30K-Hour Dataset Across 675 Underrepresented Languages for Long-Tail Multilingual Speech Technologies The paper introduces a speech dataset spanning 675 underrepresented languages to support speech recognition and text-to-speech technologies. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on multilingual speech data, not software engineering or code.
ELARSpeech: A 30K-Hour Dataset Across 675 Underrepresented Languages for Long-Tail Multilingual Speech Technologies The paper introduces a speech dataset spanning 675 underrepresented languages to support speech recognition and text-to-speech technologies. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Dataset for speech technologies, not multi-step autonomous planning agents.
ELARSpeech: A 30K-Hour Dataset Across 675 Underrepresented Languages for Long-Tail Multilingual Speech Technologies The paper introduces a speech dataset spanning 675 underrepresented languages to support speech recognition and text-to-speech technologies. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents a multilingual speech dataset rather than an LLM evaluation benchmark.
EpiGuard: An Autonomous Infectious-Disease Early Warning Agent Platform The paper presents an autonomous agent platform designed to monitor surveillance data and generate early warnings for infectious disease outbreaks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on disease monitoring agents rather than deployment AI safety risks.
EpiGuard: An Autonomous Infectious-Disease Early Warning Agent Platform The paper presents an autonomous agent platform designed to monitor surveillance data and generate early warnings for infectious disease outbreaks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Describes an applied autonomous agent platform rather than an LLM benchmark.
SERO: Learning from Executable Oracle Continuations for Long-Horizon Agentic Reinforcement Learning The paper proposes an agentic reinforcement learning method that uses executable oracle continuations to guide exploration during long-horizon tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on agent policy optimization rather than safety guardrails or alignment.
SERO: Learning from Executable Oracle Continuations for Long-Horizon Agentic Reinforcement Learning The paper proposes an agentic reinforcement learning method that uses executable oracle continuations to guide exploration during long-horizon tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents an RL training method rather than an evaluation benchmark.
KoAGE: Do Large Language Models Know, Learn, and Coin Korean Neologisms? The paper introduces a benchmark to evaluate how well language models recognize, infer, and coin newly emerging Korean terms. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on linguistic lexical evaluation rather than safety or reliability.
KoAGE: Do Large Language Models Know, Learn, and Coin Korean Neologisms? The paper introduces a benchmark to evaluate how well language models recognize, infer, and coin newly emerging Korean terms. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on language acquisition evaluation, not model compression or efficiency.
KoAGE: Do Large Language Models Know, Learn, and Coin Korean Neologisms? The paper introduces a benchmark to evaluate how well language models recognize, infer, and coin newly emerging Korean terms. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Evaluates natural language neologisms, not software code generation.
KoAGE: Do Large Language Models Know, Learn, and Coin Korean Neologisms? The paper introduces a benchmark to evaluate how well language models recognize, infer, and coin newly emerging Korean terms. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Studies vocabulary acquisition and generation, not retrieval-augmented generation.
KoAGE: Do Large Language Models Know, Learn, and Coin Korean Neologisms? The paper introduces a benchmark to evaluate how well language models recognize, infer, and coin newly emerging Korean terms. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Deals strictly with text-based neologisms, not multimodal data.
KoAGE: Do Large Language Models Know, Learn, and Coin Korean Neologisms? The paper introduces a benchmark to evaluate how well language models recognize, infer, and coin newly emerging Korean terms. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Multi-agent interaction is only a minor experimental subset for coining words.
Scaling LLM Inference via Parallelism: Throughput-Latency Trade-off The paper presents a systematic framework for selecting optimal parallelism strategies to balance latency and throughput during distributed language model serving. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Addresses distributed serving systems, not multi-step planning or tool use.
Scaling LLM Inference via Parallelism: Throughput-Latency Trade-off The paper presents a systematic framework for selecting optimal parallelism strategies to balance latency and throughput during distributed language model serving. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Covers serving systems engineering, not safety, jailbreaks, or robustness.
Scaling LLM Inference via Parallelism: Throughput-Latency Trade-off The paper presents a systematic framework for selecting optimal parallelism strategies to balance latency and throughput during distributed language model serving. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on inference systems profiling rather than general LLM benchmarking.
Vision-Language Models Integrate Conflicting Evidence Like Biased Bayesian Observers The paper proposes a Bayesian cue-combination model to explain how vision-language models integrate conflicting visual and textual evidence. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Studies vision-language perception rather than code generation or repair.
Vision-Language Models Integrate Conflicting Evidence Like Biased Bayesian Observers The paper proposes a Bayesian cue-combination model to explain how vision-language models integrate conflicting visual and textual evidence. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on evidence integration rather than compression or inference efficiency.
Vision-Language Models Integrate Conflicting Evidence Like Biased Bayesian Observers The paper proposes a Bayesian cue-combination model to explain how vision-language models integrate conflicting visual and textual evidence. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes a Bayesian predictive model rather than an evaluation benchmark.
Vision-Language Models Integrate Conflicting Evidence Like Biased Bayesian Observers The paper proposes a Bayesian cue-combination model to explain how vision-language models integrate conflicting visual and textual evidence. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies cognitive cue integration, not deployment guardrails or safety attacks.
Vision-Language Models Integrate Conflicting Evidence Like Biased Bayesian Observers The paper proposes a Bayesian cue-combination model to explain how vision-language models integrate conflicting visual and textual evidence. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Analyzes multimodal cue conflict, not external document retrieval systems.
Vision-Language Models Integrate Conflicting Evidence Like Biased Bayesian Observers The paper proposes a Bayesian cue-combination model to explain how vision-language models integrate conflicting visual and textual evidence. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Theoretical study of VLM cue combination, not industrial defect inspection.
Vision-Language Models Integrate Conflicting Evidence Like Biased Bayesian Observers The paper proposes a Bayesian cue-combination model to explain how vision-language models integrate conflicting visual and textual evidence. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Examines multimodal cue combination, not multi-step tool-using agents.
Recursive Formation and First-Class Reuse of Compositional Capabilities The paper introduces a framework for recursively composing, verifying, and persistently reusing modular agent capabilities across diverse problem domains. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses capability composition, not safety, guardrails, or alignment risks.
Recursive Formation and First-Class Reuse of Compositional Capabilities The paper introduces a framework for recursively composing, verifying, and persistently reusing modular agent capabilities across diverse problem domains. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents a functional framework rather than a general LLM evaluation benchmark.
Recursive Formation and First-Class Reuse of Compositional Capabilities The paper introduces a framework for recursively composing, verifying, and persistently reusing modular agent capabilities across diverse problem domains. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on abstract formal capabilities, not software engineering or code LLMs.
From Failures to Learnable Worlds: Policy-Conditioned Environment Generation for Navigation Agents The paper presents a method that uses agent failure patterns to generate tailored simulation environments for embodied navigation learning. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on embodied navigation policies rather than vision-language models.
From Failures to Learnable Worlds: Policy-Conditioned Environment Generation for Navigation Agents The paper presents a method that uses agent failure patterns to generate tailored simulation environments for embodied navigation learning. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on embodied robotic navigation, not industrial defect detection.
From Failures to Learnable Worlds: Policy-Conditioned Environment Generation for Navigation Agents The paper presents a method that uses agent failure patterns to generate tailored simulation environments for embodied navigation learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Generates targeted training environments, not a general evaluation benchmark.
From Failures to Learnable Worlds: Policy-Conditioned Environment Generation for Navigation Agents The paper presents a method that uses agent failure patterns to generate tailored simulation environments for embodied navigation learning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies navigation adaptation environments rather than deployment safety guardrails.
AgentSafeEvo: Adversarial Co-Evolution for LLM Agent Safety The paper introduces an adversarial co-evolution reinforcement learning framework to strengthen language model agents against tool-use manipulation attacks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces a reinforcement learning safety framework, not an evaluation benchmark.
Beyond the Trial-and-Error Loop: Hybrid Projection and Automated Tuning for Distributed Training The paper proposes a hybrid projection and simulation method to automatically configure distributed training parallelisms across GPU clusters. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents an automated systems tuning tool, not benchmark methodology.
Beyond the Trial-and-Error Loop: Hybrid Projection and Automated Tuning for Distributed Training The paper proposes a hybrid projection and simulation method to automatically configure distributed training parallelisms across GPU clusters. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on distributed cluster configuration rather than agentic tool use.
Beyond the Trial-and-Error Loop: Hybrid Projection and Automated Tuning for Distributed Training The paper proposes a hybrid projection and simulation method to automatically configure distributed training parallelisms across GPU clusters. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on training performance tuning rather than AI deployment safety.
VideoHarness-SRSI: Structured Recursive Self-Improvement for Video Understanding Harness The paper examines failure modes in recursively self-improving execution harnesses for agentic video understanding models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on harness search optimization, not safety or alignment.

3,961–4,020 of 13,754← Previous Next →