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
Does Location Matter? A Black-Box Causal Audit Reveals Model-Dependent Audio Grounding in LALM Hallucination Audits audio-language model hallucination mitigations by testing localized acoustic evidence ablation against baseline methods across model architectures. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on causal grounding and contrastive decoding, not model compression or efficient serving.
Continually Post-training Small Models via Synthetic Self-distillation Proposes a synthetic self-distillation method using a slightly larger aligner to enable continual post-training in small language models. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses catastrophic forgetting in small models rather than AI safety, guardrails, or red teaming.
Continually Post-training Small Models via Synthetic Self-distillation Proposes a synthetic self-distillation method using a slightly larger aligner to enable continual post-training in small language models. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Applies standard retention benchmarks to evaluate continual learning rather than proposing new evaluation methods.
Continually Post-training Small Models via Synthetic Self-distillation Proposes a synthetic self-distillation method using a slightly larger aligner to enable continual post-training in small language models. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on continual post-training distillation, not agentic planning, multi-step actions, or tool use.
Locate, Judge, Read: Role-Specialized Tool Agents over a Knowledge-Point Index for Open-Domain Multimodal Document QA Designs a modular multi-agent pipeline separating search, verification, and reading for multimodal document question answering. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Applies code execution solely as an internal scheduler for document QA, not software engineering.
Locate, Judge, Read: Role-Specialized Tool Agents over a Knowledge-Point Index for Open-Domain Multimodal Document QA Designs a modular multi-agent pipeline separating search, verification, and reading for multimodal document question answering. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Uses a 35B model with 3B active parameters, but efficiency is not the primary research focus.
Locate, Judge, Read: Role-Specialized Tool Agents over a Knowledge-Point Index for Open-Domain Multimodal Document QA Designs a modular multi-agent pipeline separating search, verification, and reading for multimodal document question answering. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Evaluates on existing benchmarks rather than proposing novel LLM evaluation methodologies.
Locate, Judge, Read: Role-Specialized Tool Agents over a Knowledge-Point Index for Open-Domain Multimodal Document QA Designs a modular multi-agent pipeline separating search, verification, and reading for multimodal document question answering. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on QA accuracy and retrieval orchestration rather than safety, guardrails, or reliability.
Looking Ahead and Back: Anticipatory Generative Modeling for Road-Network Trajectories Introduces an anticipatory generative modeling approach for road networks to improve realistic next-transition selection in trajectory generation. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Focuses on generative trajectory modeling methods rather than language model evaluation frameworks.
Looking Ahead and Back: Anticipatory Generative Modeling for Road-Network Trajectories Introduces an anticipatory generative modeling approach for road networks to improve realistic next-transition selection in trajectory generation. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Investigates trajectory generation on road graphs rather than LLM-based autonomous agents or tool use.
Looking Ahead and Back: Anticipatory Generative Modeling for Road-Network Trajectories Introduces an anticipatory generative modeling approach for road networks to improve realistic next-transition selection in trajectory generation. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses urban trajectory generation rather than LLM safety, guardrails, or alignment risks.
Looking Ahead and Back: Anticipatory Generative Modeling for Road-Network Trajectories Introduces an anticipatory generative modeling approach for road networks to improve realistic next-transition selection in trajectory generation. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not address model compression, quantization, distillation, or efficient LLM serving.
DP-MIND: Differentially private memory-guided Long context Inference Proposes a memory-guided differential privacy framework enabling private inference over million-token contexts in LLM-as-a-service settings. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on private long-context inference and memory mechanisms, not multi-step agent planning or tool use.
DP-MIND: Differentially private memory-guided Long context Inference Proposes a memory-guided differential privacy framework enabling private inference over million-token contexts in LLM-as-a-service settings. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes a privacy-preserving inference framework rather than evaluating models or introducing a general benchmark.
Competence-Aware Orchestration for LLM-Based Multi-Agent Systems Presents a framework that dynamically adjusts role activation and multi-agent collaboration based on the model backbone's query-specific competence. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Focuses on multi-agent orchestration architecture rather than evaluation methodology or benchmark metrics.
Competence-Aware Orchestration for LLM-Based Multi-Agent Systems Presents a framework that dynamically adjusts role activation and multi-agent collaboration based on the model backbone's query-specific competence. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not evaluate safety, jailbreaks, prompt injection, or alignment defenses.
VIGOR: Verifiable Industrial Geometry Optimization and Repair in Scene Programs Introduces an execution-grounded dataset and method to repair geometric and engineering constraint violations in LLM-generated industrial scene programs. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on scene program synthesis and geometric solver optimization rather than interactive agents.
Quality, Not Just Outcome: A Structural Scoring Framework for Selecting Coding-Agent Trajectories for Supervised Fine-Tuning Presents a structural scoring framework to select high-quality coding agent trajectories for supervised fine-tuning based on interaction efficiency. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on training data selection rather than deployment guardrails or safety red-teaming.
RoleWorld: Knowledge Transfer in World Modeling with Functional Role Abstractions Proposes a symbolic world modeling method using functional role abstractions to transfer interaction dynamics across environments for LLM agents. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Focuses on agent world modeling techniques rather than benchmark design or LLM evaluation.
RoleWorld: Knowledge Transfer in World Modeling with Functional Role Abstractions Proposes a symbolic world modeling method using functional role abstractions to transfer interaction dynamics across environments for LLM agents. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not study AI safety risks, prompt injections, guardrails, or hallucination.
AdaC-GRPO: Towards Mastering Long-Horizon Visual Sudoku Puzzles via Adaptive Curriculum Learning Introduces an adaptive curriculum reinforcement learning method to train multimodal models on long-horizon visual Sudoku reasoning tasks. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on post-training multimodal LLMs via RLVR, not autonomous multi-step tool-using agents.
AdaC-GRPO: Towards Mastering Long-Horizon Visual Sudoku Puzzles via Adaptive Curriculum Learning Introduces an adaptive curriculum reinforcement learning method to train multimodal models on long-horizon visual Sudoku reasoning tasks. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not address safety, guardrails, prompt injection, or alignment risks.
JANUS: Enhancing Long-Horizon Autonomous Research with Co-Evolving World Models Proposes JANUS, using a co-evolving world model to predict, verify, and guide exploration paths in autonomous scientific research. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces an autonomous agent framework rather than testing benchmark contamination or judge methods.
JANUS: Enhancing Long-Horizon Autonomous Research with Co-Evolving World Models Proposes JANUS, using a co-evolving world model to predict, verify, and guide exploration paths in autonomous scientific research. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not cover model compression, distillation, quantization, or efficient deployment.
JANUS: Enhancing Long-Horizon Autonomous Research with Co-Evolving World Models Proposes JANUS, using a co-evolving world model to predict, verify, and guide exploration paths in autonomous scientific research. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not investigate retrieval-augmented generation or enterprise document retrieval systems.
JANUS: Enhancing Long-Horizon Autonomous Research with Co-Evolving World Models Proposes JANUS, using a co-evolving world model to predict, verify, and guide exploration paths in autonomous scientific research. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on scientific research discovery workflows rather than software engineering or code assistants.
JANUS: Enhancing Long-Horizon Autonomous Research with Co-Evolving World Models Proposes JANUS, using a co-evolving world model to predict, verify, and guide exploration paths in autonomous scientific research. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Does not address multimodal reasoning across images, audio, or video modalities.
JANUS: Enhancing Long-Horizon Autonomous Research with Co-Evolving World Models Proposes JANUS, using a co-evolving world model to predict, verify, and guide exploration paths in autonomous scientific research. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not study AI safety guardrails, jailbreaks, or red-teaming reliability.
Does Learning Recurrent Networks Matter in Partially Observable RL? Investigates whether frozen recurrent networks can provide sufficient memory for partially observable reinforcement learning without end-to-end training. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Investigates memory in POMDPs rather than LLM benchmarking or evaluation frameworks.
Does Learning Recurrent Networks Matter in Partially Observable RL? Investigates whether frozen recurrent networks can provide sufficient memory for partially observable reinforcement learning without end-to-end training. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Studies recurrent architectures in POMDP reinforcement learning, not LLM agents.
Does Learning Recurrent Networks Matter in Partially Observable RL? Investigates whether frozen recurrent networks can provide sufficient memory for partially observable reinforcement learning without end-to-end training. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not examine safety guardrails, red teaming, hallucination, or prompt injection.
Beyond Actions: Learning Future Operation Targets for Vision-Language-Action Models Introduces AFCE, training vision-language-action policies to predict future physical effects rather than raw action chunks directly. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on robotic low-level policy action control rather than autonomous LLM agents.
Beyond Actions: Learning Future Operation Targets for Vision-Language-Action Models Introduces AFCE, training vision-language-action policies to predict future physical effects rather than raw action chunks directly. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Does not address language model evaluation benchmarks or LLM testing methodologies.
Beyond Actions: Learning Future Operation Targets for Vision-Language-Action Models Introduces AFCE, training vision-language-action policies to predict future physical effects rather than raw action chunks directly. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not address LLM safety risks, guardrails, hallucination, or adversarial attacks.
ReSFlow: Rectifying Categorical Flows on Statistical Manifolds for One-Step Generation Introduces ReSFlow, rectifying categorical flows along geodesic trajectories on statistical manifolds to enable one-step discrete generation. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on continuous generative flow mathematics, not LLM agents or planning.
ReSFlow: Rectifying Categorical Flows on Statistical Manifolds for One-Step Generation Introduces ReSFlow, rectifying categorical flows along geodesic trajectories on statistical manifolds to enable one-step discrete generation. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not cover AI alignment, security attacks, guardrails, or safety deployment.
ReSFlow: Rectifying Categorical Flows on Statistical Manifolds for One-Step Generation Introduces ReSFlow, rectifying categorical flows along geodesic trajectories on statistical manifolds to enable one-step discrete generation. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Does not study LLM evaluation benchmarks or assessment techniques.
How Reliable are AMR Parser Outputs? Uncertainty Estimation for AMR Parsing Studies uncertainty estimation for abstract meaning representation parsers using top-k beam candidates and prediction rejection ratios. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not address quantization, distillation, or efficient model compression.
How Reliable are AMR Parser Outputs? Uncertainty Estimation for AMR Parsing Studies uncertainty estimation for abstract meaning representation parsers using top-k beam candidates and prediction rejection ratios. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Studies semantic parsing graphs, not source code generation or software engineering.
How Reliable are AMR Parser Outputs? Uncertainty Estimation for AMR Parsing Studies uncertainty estimation for abstract meaning representation parsers using top-k beam candidates and prediction rejection ratios. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not involve retrieval-augmented generation or external document indexing.
How Reliable are AMR Parser Outputs? Uncertainty Estimation for AMR Parsing Studies uncertainty estimation for abstract meaning representation parsers using top-k beam candidates and prediction rejection ratios. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Does not investigate multimodal inputs like images, video, or audio.
How Reliable are AMR Parser Outputs? Uncertainty Estimation for AMR Parsing Studies uncertainty estimation for abstract meaning representation parsers using top-k beam candidates and prediction rejection ratios. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Does not study autonomous agents, tool use, or multi-agent collaboration.
Tabular Foundation Models Are Effectively Shallow Analyzes tabular foundation models and shows most transformer layers can be replaced with affine translators without sacrificing performance. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Does not evaluate multi-step planning, tool-using, or interactive agents.
Tabular Foundation Models Are Effectively Shallow Analyzes tabular foundation models and shows most transformer layers can be replaced with affine translators without sacrificing performance. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Studies model architectural depth rather than benchmark contamination or evaluation methods.
Tabular Foundation Models Are Effectively Shallow Analyzes tabular foundation models and shows most transformer layers can be replaced with affine translators without sacrificing performance. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not evaluate jailbreaks, prompt injections, safety guardrails, or alignment.
Forgetting is Not Always Bad: A Neuro-Inspired Memory Repair Mechanism for Poisoned LLM Agents Introduces DREAM, an agent-intrinsic defense that triggers functional forgetting of poisoned memory entries through implicit reactivation and topological diagnosis. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Does not focus on LLM benchmark validation, saturation, or evaluation metrics.
Agent4Spec: An LLM-based Agentic System to Optimize API Specifications through Automated Validation and Market Analysis Presents Agent4Spec, a multi-agent framework that automatically validates OpenAPI syntax errors and identifies functional API gaps through market analysis. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not study security attacks, prompt injection, or safety guardrails.
Agent4Spec: An LLM-based Agentic System to Optimize API Specifications through Automated Validation and Market Analysis Presents Agent4Spec, a multi-agent framework that automatically validates OpenAPI syntax errors and identifies functional API gaps through market analysis. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces an application system and dataset rather than studying LLM benchmark reliability.
BoundaryEvo: Data-Model Co-Evolution at the Capability Boundary for Long-Chain Reasoning Distillation Proposes BoundaryEvo, dynamically adapting training data around a student model's evolving capability boundary during long-chain reasoning distillation. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not evaluate safety, robustness, jailbreaks, or red-teaming deployments.
BoundaryEvo: Data-Model Co-Evolution at the Capability Boundary for Long-Chain Reasoning Distillation Proposes BoundaryEvo, dynamically adapting training data around a student model's evolving capability boundary during long-chain reasoning distillation. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Does not examine vision-language or multimodal cross-domain models.
BoundaryEvo: Data-Model Co-Evolution at the Capability Boundary for Long-Chain Reasoning Distillation Proposes BoundaryEvo, dynamically adapting training data around a student model's evolving capability boundary during long-chain reasoning distillation. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Does not focus on benchmark design, contamination, or LLM evaluation frameworks.
BoundaryEvo: Data-Model Co-Evolution at the Capability Boundary for Long-Chain Reasoning Distillation Proposes BoundaryEvo, dynamically adapting training data around a student model's evolving capability boundary during long-chain reasoning distillation. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Does not focus on multi-step tool-use agents or multi-agent planning systems.
BoundaryEvo: Data-Model Co-Evolution at the Capability Boundary for Long-Chain Reasoning Distillation Proposes BoundaryEvo, dynamically adapting training data around a student model's evolving capability boundary during long-chain reasoning distillation. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on general reasoning distillation, not code generation or software engineering.
BoundaryEvo: Data-Model Co-Evolution at the Capability Boundary for Long-Chain Reasoning Distillation Proposes BoundaryEvo, dynamically adapting training data around a student model's evolving capability boundary during long-chain reasoning distillation. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not address retrieval-augmented generation or grounding with external corpora.
PRIME: Pre-Retrieval Index Modeling Enhancement for Structured Document Retrieval-Real Proposes PRIME, an index-side optimization framework using semantic restructuring and deterministic metadata synthesis to improve document retrieval. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Uses standard benchmarks to evaluate retrieval rather than studying LLM evaluation methodology.
PRIME: Pre-Retrieval Index Modeling Enhancement for Structured Document Retrieval-Real Proposes PRIME, an index-side optimization framework using semantic restructuring and deterministic metadata synthesis to improve document retrieval. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not investigate safety guardrails, red-teaming, jailbreaks, or alignment failures.
PRIME: Pre-Retrieval Index Modeling Enhancement for Structured Document Retrieval-Real Proposes PRIME, an index-side optimization framework using semantic restructuring and deterministic metadata synthesis to improve document retrieval. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Mentions agents in passing; main focus is index-side retrieval enhancement for documents.
Agreement Without Alignment: Label-Free Rubric Clarification Increases LLM-Judge Consensus but Not Agreement with Humans Evaluates a label-free rubric clarification method, finding it increases consensus among LLM judges without improving agreement with human evaluations. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Does not evaluate software engineering, code generation, or program repair.
Agreement Without Alignment: Label-Free Rubric Clarification Increases LLM-Judge Consensus but Not Agreement with Humans Evaluates a label-free rubric clarification method, finding it increases consensus among LLM judges without improving agreement with human evaluations. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Studies LLM evaluation panels rather than multi-step tool-using autonomous agents.
Agreement Without Alignment: Label-Free Rubric Clarification Increases LLM-Judge Consensus but Not Agreement with Humans Evaluates a label-free rubric clarification method, finding it increases consensus among LLM judges without improving agreement with human evaluations. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Evaluates text summarization and dialogue without studying multimodal vision or audio models.

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