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
Pessimistic Latent Task-aware Optimization for Robust Offline Meta-Reinforcement Learning This paper proposes a pessimistic offline meta-reinforcement learning method that generates synthetic off-support latents to improve out-of-distribution task transfer. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on offline meta-reinforcement learning, not language model agents.
Pessimistic Latent Task-aware Optimization for Robust Offline Meta-Reinforcement Learning This paper proposes a pessimistic offline meta-reinforcement learning method that generates synthetic off-support latents to improve out-of-distribution task transfer. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses reinforcement learning distribution shifts, not deployment safety guardrails.
Pessimistic Latent Task-aware Optimization for Robust Offline Meta-Reinforcement Learning This paper proposes a pessimistic offline meta-reinforcement learning method that generates synthetic off-support latents to improve out-of-distribution task transfer. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an RL optimization algorithm, not an evaluation benchmark.
World Modeling through Spectral Alignment This paper introduces a world-modeling framework that uses kernel similarity to control which physical properties are preserved in learned embeddings. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Addresses representation geometry, not model compression or efficient serving.
World Modeling through Spectral Alignment This paper introduces a world-modeling framework that uses kernel similarity to control which physical properties are preserved in learned embeddings. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Investigates representation learning, not deployment safety or reliability risks.
World Modeling through Spectral Alignment This paper introduces a world-modeling framework that uses kernel similarity to control which physical properties are preserved in learned embeddings. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on representation learning in world models, not LLM agents.
World Modeling through Spectral Alignment This paper introduces a world-modeling framework that uses kernel similarity to control which physical properties are preserved in learned embeddings. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a representation learning method, not an evaluation benchmark.
Hidden in Plain Sight: Advancing Harmful Content Understanding via Perception-Aware Adversarial Self-Play This paper presents an adversarial self-play framework using multi-perceptual views to improve language model detection of obfuscated harmful text. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Does not address code generation or software engineering tasks.
Hidden in Plain Sight: Advancing Harmful Content Understanding via Perception-Aware Adversarial Self-Play This paper presents an adversarial self-play framework using multi-perceptual views to improve language model detection of obfuscated harmful text. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on safety evasion evasion rather than core multimodal reasoning.
Hidden in Plain Sight: Advancing Harmful Content Understanding via Perception-Aware Adversarial Self-Play This paper presents an adversarial self-play framework using multi-perceptual views to improve language model detection of obfuscated harmful text. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on adversarial content moderation, not interactive tool-use agents.
Hidden in Plain Sight: Advancing Harmful Content Understanding via Perception-Aware Adversarial Self-Play This paper presents an adversarial self-play framework using multi-perceptual views to improve language model detection of obfuscated harmful text. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces an adversarial training method, not a general evaluation benchmark.
Hidden in Plain Sight: Advancing Harmful Content Understanding via Perception-Aware Adversarial Self-Play This paper presents an adversarial self-play framework using multi-perceptual views to improve language model detection of obfuscated harmful text. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not focus on model compression, quantization, or distillation.
Hidden in Plain Sight: Advancing Harmful Content Understanding via Perception-Aware Adversarial Self-Play This paper presents an adversarial self-play framework using multi-perceptual views to improve language model detection of obfuscated harmful text. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not involve retrieval-augmented generation from external documents.
BridgeCodec: Mamba Enhanced Neural Audio Codec with Schrödinger Bridge at Low Bitrate This paper proposes an optimal-transport bridge framework to translate audio representations directly between mismatched neural codecs at low bitrates. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on neural audio codecs, not language model agents.
BridgeCodec: Mamba Enhanced Neural Audio Codec with Schrödinger Bridge at Low Bitrate This paper proposes an optimal-transport bridge framework to translate audio representations directly between mismatched neural codecs at low bitrates. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces an audio translation architecture, not an evaluation benchmark.
BridgeCodec: Mamba Enhanced Neural Audio Codec with Schrödinger Bridge at Low Bitrate This paper proposes an optimal-transport bridge framework to translate audio representations directly between mismatched neural codecs at low bitrates. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses audio cross-codec translation, not AI deployment safety.
When Messages Go Wrong: Understanding Communication Faults in LLM-Based Multi-Agent Systems This paper provides an empirical study and taxonomy of communication faults and error propagation across multi-agent language model systems. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Studies multi-agent communication networks rather than software coding models.
When Messages Go Wrong: Understanding Communication Faults in LLM-Based Multi-Agent Systems This paper provides an empirical study and taxonomy of communication faults and error propagation across multi-agent language model systems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Investigates system communication faults rather than LLM benchmarking standards.
Scalable Robust MCTS with Anytime Deterministic Optimality Guarantees This paper develops an online tree search method providing deterministic anytime bounds for robust planning under uncertain transition dynamics. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an optimization algorithm, not an evaluation benchmark.
Scalable Robust MCTS with Anytime Deterministic Optimality Guarantees This paper develops an online tree search method providing deterministic anytime bounds for robust planning under uncertain transition dynamics. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses classic MDP transition uncertainty, not deployed AI safety guardrails.
Scalable Robust MCTS with Anytime Deterministic Optimality Guarantees This paper develops an online tree search method providing deterministic anytime bounds for robust planning under uncertain transition dynamics. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on theoretical robust MDP planning algorithms, not LLM agents.
MemeLogos: Knowledge-Driven Retrieval-Augmented Reasoning for Harmful Meme Detection This paper introduces a retrieval-augmented reasoning framework that extracts recurring patterns to detect harmful multimodal social media memes. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Proposes a meme detection pipeline, not an interactive agent framework.
MemeLogos: Knowledge-Driven Retrieval-Augmented Reasoning for Harmful Meme Detection This paper introduces a retrieval-augmented reasoning framework that extracts recurring patterns to detect harmful multimodal social media memes. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a harmful meme detection method, not a benchmark.
SkillFusion: Towards Reliable Multi-Skill Composition for LLM Agents This paper introduces an offline compiler that detects conflicts and merges multiple agent skills into a single coherent capability. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses multi-skill composition rather than deployment safety and robustness.
SkillFusion: Towards Reliable Multi-Skill Composition for LLM Agents This paper introduces an offline compiler that detects conflicts and merges multiple agent skills into a single coherent capability. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on skill merging architecture rather than evaluation benchmarks.
Security in LLM-Assisted Coding: A Systematic Literature Review This systematic review analyzes security vulnerabilities, benchmark shortcomings, and developer oversight challenges in code generated by language models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Surveys code security rather than general multi-step agent frameworks.
When Skills Remain But Access Fails: Factorized Skill Memory for Continual Skill Internalization This paper investigates catastrophic forgetting in continual agent skill learning and proposes a factorized memory architecture to preserve procedural execution. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses continual learning of skills rather than safety guardrails.
When Skills Remain But Access Fails: Factorized Skill Memory for Continual Skill Internalization This paper investigates catastrophic forgetting in continual agent skill learning and proposes a factorized memory architecture to preserve procedural execution. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a skill memory architecture, not an evaluation framework.
The Imperishable Record, Forgiveness, and the Future of Criminal Justice This paper examines how persistent digital records and AI influence individual futures, proposing legal and conceptual limits on the eternal use of criminal data. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Legal and philosophical analysis of criminal records, not agent systems.
The Imperishable Record, Forgiveness, and the Future of Criminal Justice This paper examines how persistent digital records and AI influence individual futures, proposing legal and conceptual limits on the eternal use of criminal data. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Addresses criminal justice ethics, not model evaluation or benchmarks.
The Imperishable Record, Forgiveness, and the Future of Criminal Justice This paper examines how persistent digital records and AI influence individual futures, proposing legal and conceptual limits on the eternal use of criminal data. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Presents legal and philosophical arguments, not deployment safety engineering.
Large Decision Models: Repurposing Large Language Models as Single-Pass Decision Makers This paper adapts language models into single-pass decision-makers that output explicit probability distributions over task labels specified at run time. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on single-pass classification methods rather than multi-step agent planning.
Large Decision Models: Repurposing Large Language Models as Single-Pass Decision Makers This paper adapts language models into single-pass decision-makers that output explicit probability distributions over task labels specified at run time. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on inference speedup and decision schemas, not safety.
Large Decision Models: Repurposing Large Language Models as Single-Pass Decision Makers This paper adapts language models into single-pass decision-makers that output explicit probability distributions over task labels specified at run time. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a classification decision model, not an LLM evaluation methodology.
Data Portability Revisited: Toward the Human-Centric, AI-Driven Data Ecosystems of Tomorrow This article analyzes current legal and technical frameworks for data portability, arguing for a human-centric approach to personal data governance in AI. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Examines legal data rights rather than AI deployment safety risks.
Data Portability Revisited: Toward the Human-Centric, AI-Driven Data Ecosystems of Tomorrow This article analyzes current legal and technical frameworks for data portability, arguing for a human-centric approach to personal data governance in AI. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Discusses regulatory data frameworks, not LLM benchmark evaluation.
Data Portability Revisited: Toward the Human-Centric, AI-Driven Data Ecosystems of Tomorrow This article analyzes current legal and technical frameworks for data portability, arguing for a human-centric approach to personal data governance in AI. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on legal and regulatory data portability policy, not agent planning.
CacheLink: Functional KV Transport for Zero-Prefill Cross-Model Context Handoff This paper introduces a method to translate key-value caches between different language models, enabling context handoffs without reprocessing input tokens. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a cache serving method, not an evaluation benchmark.
CacheLink: Functional KV Transport for Zero-Prefill Cross-Model Context Handoff This paper introduces a method to translate key-value caches between different language models, enabling context handoffs without reprocessing input tokens. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on KV cache serving efficiency, not deployment safety.
CacheLink: Functional KV Transport for Zero-Prefill Cross-Model Context Handoff This paper introduces a method to translate key-value caches between different language models, enabling context handoffs without reprocessing input tokens. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies KV cache transport across models, not agent planning.
REPRO: Reflective Evolutionary Prompt Optimizer for Automated LLM Red-Teaming This paper proposes a black-box evolutionary prompt optimization framework using a small open-weight model to discover adversarial jailbreaks against language models. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text safety prompts, not multimodal models.
REPRO: Reflective Evolutionary Prompt Optimizer for Automated LLM Red-Teaming This paper proposes a black-box evolutionary prompt optimization framework using a small open-weight model to discover adversarial jailbreaks against language models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Automated prompt optimization, not autonomous multi-step tool-using agents.
REPRO: Reflective Evolutionary Prompt Optimizer for Automated LLM Red-Teaming This paper proposes a black-box evolutionary prompt optimization framework using a small open-weight model to discover adversarial jailbreaks against language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Adversarial prompt search, not retrieval-augmented generation.
REPRO: Reflective Evolutionary Prompt Optimizer for Automated LLM Red-Teaming This paper proposes a black-box evolutionary prompt optimization framework using a small open-weight model to discover adversarial jailbreaks against language models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Uses a 7B model for optimization, but does not study compression.
REPRO: Reflective Evolutionary Prompt Optimizer for Automated LLM Red-Teaming This paper proposes a black-box evolutionary prompt optimization framework using a small open-weight model to discover adversarial jailbreaks against language models. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on adversarial jailbreaks, not software engineering models.
SARATHI: Decoupled Data-Free Probing and Adaptive Reconstruction for Structured LLM Pruning This paper presents a structured pruning framework that decouples data-free neuron selection from localized weight reconstruction to reduce model memory requirements. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Structured pruning method, not retrieval-augmented generation.
SARATHI: Decoupled Data-Free Probing and Adaptive Reconstruction for Structured LLM Pruning This paper presents a structured pruning framework that decouples data-free neuron selection from localized weight reconstruction to reduce model memory requirements. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses model weight pruning, not agentic planning or tools.
SARATHI: Decoupled Data-Free Probing and Adaptive Reconstruction for Structured LLM Pruning This paper presents a structured pruning framework that decouples data-free neuron selection from localized weight reconstruction to reduce model memory requirements. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Compression method for LLMs, not code-specific software engineering.
SARATHI: Decoupled Data-Free Probing and Adaptive Reconstruction for Structured LLM Pruning This paper presents a structured pruning framework that decouples data-free neuron selection from localized weight reconstruction to reduce model memory requirements. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Evaluated on text LLMs, not multimodal vision-language architectures.
SARATHI: Decoupled Data-Free Probing and Adaptive Reconstruction for Structured LLM Pruning This paper presents a structured pruning framework that decouples data-free neuron selection from localized weight reconstruction to reduce model memory requirements. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on post-training model compression, not safety.
SARATHI: Decoupled Data-Free Probing and Adaptive Reconstruction for Structured LLM Pruning This paper presents a structured pruning framework that decouples data-free neuron selection from localized weight reconstruction to reduce model memory requirements. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a pruning technique, not an LLM benchmark.
OceanFlow: Coherent Flow Matching for Probabilistic Ocean Modeling The paper proposes a flow matching framework for probabilistic global ocean modeling to generate calibrated conditional ensembles. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on geophysical dynamics, not AI deployment safety.
OceanFlow: Coherent Flow Matching for Probabilistic Ocean Modeling The paper proposes a flow matching framework for probabilistic global ocean modeling to generate calibrated conditional ensembles. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Deals with probabilistic climate modeling, not model compression.
OceanFlow: Coherent Flow Matching for Probabilistic Ocean Modeling The paper proposes a flow matching framework for probabilistic global ocean modeling to generate calibrated conditional ensembles. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on physical ocean modeling, not LLM evaluation benchmarks.
OceanFlow: Coherent Flow Matching for Probabilistic Ocean Modeling The paper proposes a flow matching framework for probabilistic global ocean modeling to generate calibrated conditional ensembles. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Concerns oceanographic flow matching, not LLM autonomous agents.
Improving the Bounds on Approximating Weighted Maximin Share for Chores The paper establishes improved approximation bounds and polynomial-time algorithms for allocating indivisible chores fairly among weighted agents. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Theoretical chore allocation paper, not an LLM evaluation benchmark.
Improving the Bounds on Approximating Weighted Maximin Share for Chores The paper establishes improved approximation bounds and polynomial-time algorithms for allocating indivisible chores fairly among weighted agents. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Theoretical game theory algorithm, not LLM-based autonomous agents.
Improving the Bounds on Approximating Weighted Maximin Share for Chores The paper establishes improved approximation bounds and polynomial-time algorithms for allocating indivisible chores fairly among weighted agents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies mathematical fair division, not AI safety or robustness.
ELECT: Settling Identity Before Rendering for Scalable Story Visualization The paper proposes a training-free method establishing entity identity before final rendering to maintain visual consistency across long storyboards. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a visual rendering method, not an evaluation benchmark.
ELECT: Settling Identity Before Rendering for Scalable Story Visualization The paper proposes a training-free method establishing entity identity before final rendering to maintain visual consistency across long storyboards. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on generative story rendering, not autonomous LLM agents.

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