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
Query Your Way Out: Benchmarking Multi-Turn Knowledge Reasoning through Generalized Puzzle Solving This paper introduces the Active Probing Benchmark to evaluate multi-turn active questioning and specialized domain reasoning in language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on reasoning evaluation rather than deployment safety risks.
Query Your Way Out: Benchmarking Multi-Turn Knowledge Reasoning through Generalized Puzzle Solving This paper introduces the Active Probing Benchmark to evaluate multi-turn active questioning and specialized domain reasoning in language models. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Evaluates language reasoning without multimodal vision or audio components.
Query Your Way Out: Benchmarking Multi-Turn Knowledge Reasoning through Generalized Puzzle Solving This paper introduces the Active Probing Benchmark to evaluate multi-turn active questioning and specialized domain reasoning in language models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not study model compression or efficient serving.
Query Your Way Out: Benchmarking Multi-Turn Knowledge Reasoning through Generalized Puzzle Solving This paper introduces the Active Probing Benchmark to evaluate multi-turn active questioning and specialized domain reasoning in language models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Presents an interactive reasoning benchmark rather than agent deployment.
Omni-Modal LLMs Know More Than They Say This paper analyzes why omni-modal language models perceive non-textual inputs correctly yet fail to mention them in text outputs. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not related to programming, code generation, or software engineering.
Omni-Modal LLMs Know More Than They Say This paper analyzes why omni-modal language models perceive non-textual inputs correctly yet fail to mention them in text outputs. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on alignment methodology rather than evaluation benchmarks.
Omni-Modal LLMs Know More Than They Say This paper analyzes why omni-modal language models perceive non-textual inputs correctly yet fail to mention them in text outputs. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not study model compression, distillation, or quantization.
Omni-Modal LLMs Know More Than They Say This paper analyzes why omni-modal language models perceive non-textual inputs correctly yet fail to mention them in text outputs. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on multimodal token alignment rather than autonomous agents.
Omni-Modal LLMs Know More Than They Say This paper analyzes why omni-modal language models perceive non-textual inputs correctly yet fail to mention them in text outputs. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not focus on document retrieval or grounding pipelines.
Omni-Modal LLMs Know More Than They Say This paper analyzes why omni-modal language models perceive non-textual inputs correctly yet fail to mention them in text outputs. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Studies omni-modal representations, not industrial inspection.
Omni-Modal LLMs Know More Than They Say This paper analyzes why omni-modal language models perceive non-textual inputs correctly yet fail to mention them in text outputs. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies cross-modal expression rather than safety guardrails.
Large Language Model Assisted Transportation Planning through a Differentiable User Equilibrium Layer This paper combines language models with a differentiable traffic equilibrium layer to help non-experts optimize transportation planning problems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Describes an optimization application rather than an evaluation benchmark.
Large Language Model Assisted Transportation Planning through a Differentiable User Equilibrium Layer This paper combines language models with a differentiable traffic equilibrium layer to help non-experts optimize transportation planning problems. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on domain-specific transportation optimization rather than AI safety.
Phantom Merge: When Your Large Language Model Agents Pick One but Tell You About Another This paper identifies and mitigates Phantom Merge, an error where language model agents bind retrieved facts to the wrong entity. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on error analysis and mitigation rather than a benchmark.
Phantom Merge: When Your Large Language Model Agents Pick One but Tell You About Another This paper identifies and mitigates Phantom Merge, an error where language model agents bind retrieved facts to the wrong entity. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model efficiency, pruning, or quantization.
Phantom Merge: When Your Large Language Model Agents Pick One but Tell You About Another This paper identifies and mitigates Phantom Merge, an error where language model agents bind retrieved facts to the wrong entity. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not focused on code generation or software engineering tasks.
Phantom Merge: When Your Large Language Model Agents Pick One but Tell You About Another This paper identifies and mitigates Phantom Merge, an error where language model agents bind retrieved facts to the wrong entity. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Deals with agent entity-binding errors rather than core RAG architectures.
Phantom Merge: When Your Large Language Model Agents Pick One but Tell You About Another This paper identifies and mitigates Phantom Merge, an error where language model agents bind retrieved facts to the wrong entity. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on textual agent reasoning, not multimodal data.
Pollard: Governed Semantic Execution Trees for Framework-Independent AI Agent Control and Replay This paper presents Pollard, a framework-independent agent runtime that uses semantic execution trees for deterministic tracking, governance, and replay. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent runtime infrastructure, not evaluation benchmarks.
Unravel: Recovering an Interactive 3D Scene from a Single Image This paper introduces Unravel, a training-free framework that converts a single image into an editable, multi-object 3D scene. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a 3D reconstruction system, not an evaluation benchmark.
Unravel: Recovering an Interactive 3D Scene from a Single Image This paper introduces Unravel, a training-free framework that converts a single image into an editable, multi-object 3D scene. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Uses agentic harness narrowly for 3D graphics reconstruction.
Unravel: Recovering an Interactive 3D Scene from a Single Image This paper introduces Unravel, a training-free framework that converts a single image into an editable, multi-object 3D scene. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on 3D computer graphics reconstruction, not safety.
Unravel: Recovering an Interactive 3D Scene from a Single Image This paper introduces Unravel, a training-free framework that converts a single image into an editable, multi-object 3D scene. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on 3D scene decomposition, not industrial defect inspection.
WildTrace: Evaluating Training Data Detection in Realistic Large-Scale LLM Post-Training This paper introduces WildTrace, a benchmark evaluating training data detection methods across realistic, multi-stage language model post-training pipelines. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies training data detection rather than agent execution.
WildTrace: Evaluating Training Data Detection in Realistic Large-Scale LLM Post-Training This paper introduces WildTrace, a benchmark evaluating training data detection methods across realistic, multi-stage language model post-training pipelines. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression or efficient inference.
Fisher information and the geometry of memorization in neural networks This paper analyzes Fisher information eigenvectors to show how neural networks geometrically decouple generalizable patterns from memorized noisy data. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on learning theory rather than model compression techniques.
Fisher information and the geometry of memorization in neural networks This paper analyzes Fisher information eigenvectors to show how neural networks geometrically decouple generalizable patterns from memorized noisy data. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies theoretical loss geometry rather than agent systems.
Fisher information and the geometry of memorization in neural networks This paper analyzes Fisher information eigenvectors to show how neural networks geometrically decouple generalizable patterns from memorized noisy data. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Theoretical analysis of memorization, not an evaluation benchmark.
Fisher information and the geometry of memorization in neural networks This paper analyzes Fisher information eigenvectors to show how neural networks geometrically decouple generalizable patterns from memorized noisy data. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Theoretical study of capacity allocation, not deployment safety.
Interactive 4D Volumetric Liquid Forecasting under Moving-Solid Interaction This paper proposes a task and framework to forecast dense 3D liquid velocity fields based on how solid objects move through them. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Deals with fluid dynamics simulation rather than AI safety.
Interactive 4D Volumetric Liquid Forecasting under Moving-Solid Interaction This paper proposes a task and framework to forecast dense 3D liquid velocity fields based on how solid objects move through them. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a physics modeling task, not an LLM benchmark.
Interactive 4D Volumetric Liquid Forecasting under Moving-Solid Interaction This paper proposes a task and framework to forecast dense 3D liquid velocity fields based on how solid objects move through them. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies physical fluid dynamics forecasting, not LLM agents.
Interactive 4D Volumetric Liquid Forecasting under Moving-Solid Interaction This paper proposes a task and framework to forecast dense 3D liquid velocity fields based on how solid objects move through them. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression or efficient serving.
EventLens: Event-Structure Reinforcement Learning for Video Understanding This paper presents EventLens, a reinforcement learning framework that trains multimodal models to understand video through explicit event structure recovery. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a reinforcement learning framework, not a general evaluation benchmark.
EventLens: Event-Structure Reinforcement Learning for Video Understanding This paper presents EventLens, a reinforcement learning framework that trains multimodal models to understand video through explicit event structure recovery. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses temporal representation learning rather than safety or guardrails.
EventLens: Event-Structure Reinforcement Learning for Video Understanding This paper presents EventLens, a reinforcement learning framework that trains multimodal models to understand video through explicit event structure recovery. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on video representation learning rather than multi-step agent planning.
EventLens: Event-Structure Reinforcement Learning for Video Understanding This paper presents EventLens, a reinforcement learning framework that trains multimodal models to understand video through explicit event structure recovery. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Evaluates general video understanding rather than industrial inspection.
ProCARE: Real-World Study Automation via Profile-Grounded Evidence Contracts This paper proposes ProCARE, a framework that uses structured contracts to reliably automate healthcare observational studies with language model agents. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on execution framework rather than general LLM evaluation.
Label-Centric Knowledge Retrieval for Zero-Shot Text Classification This paper proposes a framework that precomputes label definitions and boundaries with large models to enable cheaper zero-shot text classification. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on classification efficiency, not safety or guardrails.
Label-Centric Knowledge Retrieval for Zero-Shot Text Classification This paper proposes a framework that precomputes label definitions and boundaries with large models to enable cheaper zero-shot text classification. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on zero-shot classification retrieval, not multi-step agents.
Label-Centric Knowledge Retrieval for Zero-Shot Text Classification This paper proposes a framework that precomputes label definitions and boundaries with large models to enable cheaper zero-shot text classification. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a classification method, not an evaluation benchmark.
Correct Choices, Failed Sequences: Choice2Seq Benchmarks Correctness Retention Across Output Interfaces This paper introduces Choice2Seq, a benchmark measuring how often language models retain correct multiple-choice answers when generating them step by step. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Evaluates interface formatting consistency rather than deployment safety risks.
Correct Choices, Failed Sequences: Choice2Seq Benchmarks Correctness Retention Across Output Interfaces This paper introduces Choice2Seq, a benchmark measuring how often language models retain correct multiple-choice answers when generating them step by step. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Evaluates interface outputs, not tool-using or autonomous agents.
MP-MNCA: Morphology-Prior Masked Neighbor Cross-Attention for Efficient Spatial Domain Identification This paper presents a cross-attention method with histological priors to improve training efficiency and convergence in spatial transcriptomics. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Biological modeling unrelated to deployment safety and robustness.
MP-MNCA: Morphology-Prior Masked Neighbor Cross-Attention for Efficient Spatial Domain Identification This paper presents a cross-attention method with histological priors to improve training efficiency and convergence in spatial transcriptomics. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Biology application model, not an LLM evaluation method.
MP-MNCA: Morphology-Prior Masked Neighbor Cross-Attention for Efficient Spatial Domain Identification This paper presents a cross-attention method with histological priors to improve training efficiency and convergence in spatial transcriptomics. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Spatial transcriptomics method, not an AI agent system.
MP-MNCA: Morphology-Prior Masked Neighbor Cross-Attention for Efficient Spatial Domain Identification This paper presents a cross-attention method with histological priors to improve training efficiency and convergence in spatial transcriptomics. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on biological domain identification, not model compression.
MP-MNCA: Morphology-Prior Masked Neighbor Cross-Attention for Efficient Spatial Domain Identification This paper presents a cross-attention method with histological priors to improve training efficiency and convergence in spatial transcriptomics. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Deals with spatial transcriptomics data, not multimodal language models.
MP-MNCA: Morphology-Prior Masked Neighbor Cross-Attention for Efficient Spatial Domain Identification This paper presents a cross-attention method with histological priors to improve training efficiency and convergence in spatial transcriptomics. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Applies to biological transcriptomics, not industrial visual inspection.
SafeDSE: Joint Exploration of Safety Architecture Patterns, Mapping, and Scheduling for Dataflow Applications With Safety Arguments (Extended Version) Presents a design space exploration tool for mapping, scheduling, and generating safety arguments for dataflow applications on embedded hardware platforms. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on embedded hardware dataflow architectures, not AI deployment safety.
SafeDSE: Joint Exploration of Safety Architecture Patterns, Mapping, and Scheduling for Dataflow Applications With Safety Arguments (Extended Version) Presents a design space exploration tool for mapping, scheduling, and generating safety arguments for dataflow applications on embedded hardware platforms. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses embedded system scheduling, not language model agents.
SafeDSE: Joint Exploration of Safety Architecture Patterns, Mapping, and Scheduling for Dataflow Applications With Safety Arguments (Extended Version) Presents a design space exploration tool for mapping, scheduling, and generating safety arguments for dataflow applications on embedded hardware platforms. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Embedded hardware exploration framework, not an LLM benchmark.
SafeDSE: Joint Exploration of Safety Architecture Patterns, Mapping, and Scheduling for Dataflow Applications With Safety Arguments (Extended Version) Presents a design space exploration tool for mapping, scheduling, and generating safety arguments for dataflow applications on embedded hardware platforms. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not related to LLMs writing or fixing code.
OneShot: Label-Efficient Detector Weighting for Time Series Anomaly Detection Weights an ensemble of time-series anomaly detectors using confirmation of a single reported anomaly from a human operator. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant General time-series work unrelated to LLM deployment safety.
OneShot: Label-Efficient Detector Weighting for Time Series Anomaly Detection Weights an ensemble of time-series anomaly detectors using confirmation of a single reported anomaly from a human operator. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a detector ensembling method, not an LLM benchmark.
OneShot: Label-Efficient Detector Weighting for Time Series Anomaly Detection Weights an ensemble of time-series anomaly detectors using confirmation of a single reported anomaly from a human operator. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on time-series ensembling, not model compression.
OneShot: Label-Efficient Detector Weighting for Time Series Anomaly Detection Weights an ensemble of time-series anomaly detectors using confirmation of a single reported anomaly from a human operator. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses time-series anomaly detection, not LLM agents.
Look Back to Move Forward: Learning to Revisit for Agentic Compositional Image Generation Develops an agentic text-to-image framework allowing agents to revisit and select earlier image states for iterative compositional generation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an agent framework, not a benchmark.
Look Back to Move Forward: Learning to Revisit for Agentic Compositional Image Generation Develops an agentic text-to-image framework allowing agents to revisit and select earlier image states for iterative compositional generation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on image editing performance, not AI safety.
Look Back to Move Forward: Learning to Revisit for Agentic Compositional Image Generation Develops an agentic text-to-image framework allowing agents to revisit and select earlier image states for iterative compositional generation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on generative image composition, not industrial defect detection.

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