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
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on skill merging architecture rather than evaluation benchmarks.
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.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a skill memory architecture, not an evaluation framework.
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.
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.
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.
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.
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.
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.
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 safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Examines legal data rights rather than AI deployment safety risks.
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 agents via arxiv · 15 Oct, 06:17	Not relevant Studies KV cache transport across models, 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	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on KV cache serving efficiency, not deployment safety.
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.
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	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	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	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Uses a 7B model for optimization, but does not study compression.
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 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	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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a pruning technique, not an LLM benchmark.
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.
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.
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	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	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Deals with probabilistic climate modeling, not model compression.
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 safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies mathematical fair division, not AI safety or robustness.
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.
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	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Generates visual story frames, not industrial visual inspection.
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 safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Concerns story visualization efficiency, not AI safety.
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.
Self-Representation Alignment in Diffusion Models for Video Background Music Generation The paper introduces an internal feature alignment loss for diffusion models to improve background music generation from video inputs. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Deals with video-to-music diffusion models, not autonomous agents.
Self-Representation Alignment in Diffusion Models for Video Background Music Generation The paper introduces an internal feature alignment loss for diffusion models to improve background music generation from video inputs. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses generative media alignment, not AI deployment safety.
Self-Representation Alignment in Diffusion Models for Video Background Music Generation The paper introduces an internal feature alignment loss for diffusion models to improve background music generation from video inputs. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a generative audio-visual architecture, not an evaluation benchmark.
Inverse Garment Design with Visual Agents The paper introduces an agentic system that reconstructs multi-layer 3D garment sewing patterns from a single image using simulation feedback. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on garment modeling, not AI safety or guardrails.
Inverse Garment Design with Visual Agents The paper introduces an agentic system that reconstructs multi-layer 3D garment sewing patterns from a single image using simulation feedback. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an inverse design framework, not an evaluation benchmark.
CondMSP: Condition-Informed and Resource-Aware Inorganic Materials Synthesis Planning The paper presents an inorganic materials synthesis framework that incorporates condition prediction as context to guide reaction route planning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Addresses materials science route planning, not LLM evaluation.
CondMSP: Condition-Informed and Resource-Aware Inorganic Materials Synthesis Planning The paper presents an inorganic materials synthesis framework that incorporates condition prediction as context to guide reaction route planning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Covers materials discovery algorithms, not AI safety risks.
CondMSP: Condition-Informed and Resource-Aware Inorganic Materials Synthesis Planning The paper presents an inorganic materials synthesis framework that incorporates condition prediction as context to guide reaction route planning. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Domain-specific materials synthesis planning, not general LLM agents.
From Stages to States: Unified Iterative Reasoning for MLLMs The paper proposes an iterative reasoning framework that models intermediate multimodal reasoning steps as dynamic state transitions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on multimodal reasoning performance, not deployment safety.
From Stages to States: Unified Iterative Reasoning for MLLMs The paper proposes an iterative reasoning framework that models intermediate multimodal reasoning steps as dynamic state transitions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a multimodal reasoning technique, not an evaluation framework.
From Stages to States: Unified Iterative Reasoning for MLLMs The paper proposes an iterative reasoning framework that models intermediate multimodal reasoning steps as dynamic state transitions. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses internal reasoning chains, not tool-using or autonomous agents.
Big-Big-SycoBench: A Benchmark to Detect Sycophancy Through Model Responses to Valid vs. Invalid Arguments The paper introduces a benchmark measuring whether language models resist sycophancy when presented with invalid user arguments versus valid critiques. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Measures model sycophancy, not multi-step planning or tool use.
Recall What Is Missing: Composing Evidence from Long-Term Agent Memory The paper presents a training-free memory selection method that progressively composes non-redundant evidence sets for long-term agents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on memory retrieval efficiency, not deployment safety.
Recall What Is Missing: Composing Evidence from Long-Term Agent Memory The paper presents a training-free memory selection method that progressively composes non-redundant evidence sets for long-term agents. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates a memory mechanism, not proposing general LLM benchmarks.
Dynamic Conflict Clustering with Lightweight Learning and Strategic Repair for Distributed Constraint Satisfaction The paper introduces a conflict-clustering solver with lightweight learning to coordinate distributed agents solving constraint satisfaction problems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a DCSP solver, not an LLM evaluation benchmark.
Dynamic Conflict Clustering with Lightweight Learning and Strategic Repair for Distributed Constraint Satisfaction The paper introduces a conflict-clustering solver with lightweight learning to coordinate distributed agents solving constraint satisfaction problems. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on distributed constraint solving algorithms, not safety.
Knowledge Interaction in Hypernetwork-based Continual Learning: Conflict and Composition The paper examines how hypernetwork adapters resolve knowledge conflicts and compose information during continual learning in language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Analyzes internal knowledge interactions, not evaluation benchmarking.
Knowledge Interaction in Hypernetwork-based Continual Learning: Conflict and Composition The paper examines how hypernetwork adapters resolve knowledge conflicts and compose information during continual learning in language models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Examines continual learning and hypernetworks, not LLM agents.

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