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
AphaVoice: Text-to-Speech Model for Aphasia Patient Simulation The paper develops a text-to-speech model that simulates realistic clinical dysfluency in aphasia speech across multiple speakers for training healthcare professionals. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression, pruning, or efficient inference.
AphaVoice: Text-to-Speech Model for Aphasia Patient Simulation The paper develops a text-to-speech model that simulates realistic clinical dysfluency in aphasia speech across multiple speakers for training healthcare professionals. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on clinical patient speech simulation, not safety guardrails.
AphaVoice: Text-to-Speech Model for Aphasia Patient Simulation The paper develops a text-to-speech model that simulates realistic clinical dysfluency in aphasia speech across multiple speakers for training healthcare professionals. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Develops a speech synthesis model, not autonomous LLM agents.
AphaVoice: Text-to-Speech Model for Aphasia Patient Simulation The paper develops a text-to-speech model that simulates realistic clinical dysfluency in aphasia speech across multiple speakers for training healthcare professionals. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on speech synthesis, not retrieval-augmented generation.
AphaVoice: Text-to-Speech Model for Aphasia Patient Simulation The paper develops a text-to-speech model that simulates realistic clinical dysfluency in aphasia speech across multiple speakers for training healthcare professionals. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Covers speech generation rather than software engineering or coding.
AlignPoison: Conditional Targeted Poisoning of Generative Recommenders via Decoding-Aligned Prefixes The paper demonstrates that generative recommenders are vulnerable to targeted poisoning attacks using fewer than 1% malicious synthetic interactions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Examines adversarial poisoning rather than LLM evaluation benchmarks.
AlignPoison: Conditional Targeted Poisoning of Generative Recommenders via Decoding-Aligned Prefixes The paper demonstrates that generative recommenders are vulnerable to targeted poisoning attacks using fewer than 1% malicious synthetic interactions. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on poisoning recommender systems rather than LLM agents.
HiPPT: Physiology-Grounded Wearable PPG Representation Learning via Multi-Scope Text Alignment The paper presents a pretraining framework aligning wearable PPG signals with text across both individual pulse morphologies and broader demographic contexts. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Covers biomedical wearable modeling, not AI deployment safety.
HiPPT: Physiology-Grounded Wearable PPG Representation Learning via Multi-Scope Text Alignment The paper presents a pretraining framework aligning wearable PPG signals with text across both individual pulse morphologies and broader demographic contexts. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on sensor representation learning rather than LLM benchmarks.
HiPPT: Physiology-Grounded Wearable PPG Representation Learning via Multi-Scope Text Alignment The paper presents a pretraining framework aligning wearable PPG signals with text across both individual pulse morphologies and broader demographic contexts. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Presents wearable PPG physiological representation learning, not LLM agents.
Reels-RSI: Program-Level Self-Improvement for Code-Rendered Explainer Videos The paper introduces an open-source agent framework that improves code-rendered explainer videos through program-level debugging, static rules, and unit testing. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses program synthesis workflow rather than deployment safety or guardrails.
Reels-RSI: Program-Level Self-Improvement for Code-Rendered Explainer Videos The paper introduces an open-source agent framework that improves code-rendered explainer videos through program-level debugging, static rules, and unit testing. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on a self-improving coding agent system rather than LLM benchmarking.
Reels-RSI: Program-Level Self-Improvement for Code-Rendered Explainer Videos The paper introduces an open-source agent framework that improves code-rendered explainer videos through program-level debugging, static rules, and unit testing. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Generates code for animation renderers rather than multimodal model architectures.
Rethinking Activation Functions: Reducing Bias Can Improve Learning The paper examines positive activation bias in neural networks, showing that reducing this bias slows initial training but improves final model performance. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on neural network training dynamics, not deployment safety.
Rethinking Activation Functions: Reducing Bias Can Improve Learning The paper examines positive activation bias in neural networks, showing that reducing this bias slows initial training but improves final model performance. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies optimization dynamics rather than benchmark evaluation frameworks.
Rethinking Activation Functions: Reducing Bias Can Improve Learning The paper examines positive activation bias in neural networks, showing that reducing this bias slows initial training but improves final model performance. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on activation function mathematics, not autonomous LLM agents.
MontageFlow: Physically Valid High Fidelity EEG Generation The paper introduces a flow-matching model that enforces exact linear identity constraints among EEG channels to generate physically valid clinical signals. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on generative signal modeling rather than LLM benchmarking.
MontageFlow: Physically Valid High Fidelity EEG Generation The paper introduces a flow-matching model that enforces exact linear identity constraints among EEG channels to generate physically valid clinical signals. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Concerns EEG signal generation via flow matching, not LLM agents.
MontageFlow: Physically Valid High Fidelity EEG Generation The paper introduces a flow-matching model that enforces exact linear identity constraints among EEG channels to generate physically valid clinical signals. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Pertains to biomedical signal generation, not LLM deployment safety.
Compositional Concepts Erasing: Disentangling Object-Style via Domain Manifold Unlearning The paper presents a feature-proxy method to selectively erase combined object-style concepts from diffusion models without degrading general generative performance. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Pertains to generative diffusion models, not industrial inspection vision.
Compositional Concepts Erasing: Disentangling Object-Style via Domain Manifold Unlearning The paper presents a feature-proxy method to selectively erase combined object-style concepts from diffusion models without degrading general generative performance. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses concept unlearning in image diffusion, not LLM agents.
Compositional Concepts Erasing: Disentangling Object-Style via Domain Manifold Unlearning The paper presents a feature-proxy method to selectively erase combined object-style concepts from diffusion models without degrading general generative performance. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses specifically on image diffusion unlearning rather than multimodal understanding.
Compositional Concepts Erasing: Disentangling Object-Style via Domain Manifold Unlearning The paper presents a feature-proxy method to selectively erase combined object-style concepts from diffusion models without degrading general generative performance. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on machine unlearning methods rather than benchmark evaluation.
ScienceBuddy-PV: A Verifier-Governed Recursive Improvement Protocol for Panvascular Intervention Research The paper specifies a verifier-governed protocol for panvascular research and benchmarks a baseline model on intervention imaging tasks. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Evaluates a medical image segmentation model and gate, not LLM agents.
ScienceBuddy-PV: A Verifier-Governed Recursive Improvement Protocol for Panvascular Intervention Research The paper specifies a verifier-governed protocol for panvascular research and benchmarks a baseline model on intervention imaging tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates medical computer vision components rather than LLM benchmarks.
ScienceBuddy-PV: A Verifier-Governed Recursive Improvement Protocol for Panvascular Intervention Research The paper specifies a verifier-governed protocol for panvascular research and benchmarks a baseline model on intervention imaging tasks. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on medical image segmentation, not multimodal vision-language models.
ScienceBuddy-PV: A Verifier-Governed Recursive Improvement Protocol for Panvascular Intervention Research The paper specifies a verifier-governed protocol for panvascular research and benchmarks a baseline model on intervention imaging tasks. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on clinical vascular medical imaging, not industrial manufacturing inspection.
Energy Ring: Stabilizing Feature Energy for Reliable Test-Time Discovery The paper proposes an energy-anchoring technique to reliably separate known and novel classes during continual test-time model adaptation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a representation discovery method rather than benchmark evaluation.
Energy Ring: Stabilizing Feature Energy for Reliable Test-Time Discovery The paper proposes an energy-anchoring technique to reliably separate known and novel classes during continual test-time model adaptation. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Deals with test-time classification discovery, not LLM planning agents.
Who to Trust in Multi-Robot Systems: Joint Robot and Object Hypothesis Trust Inference via Neural-Symbolic Cross-Validation The paper presents a neural-symbolic method for multi-robot systems to evaluate robot and object hypothesis trust purely via inter-robot observation consistency. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on multi-robot sensory trust estimation rather than LLM agents.
Who to Trust in Multi-Robot Systems: Joint Robot and Object Hypothesis Trust Inference via Neural-Symbolic Cross-Validation The paper presents a neural-symbolic method for multi-robot systems to evaluate robot and object hypothesis trust purely via inter-robot observation consistency. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a Bayesian trust inference framework rather than LLM benchmark evaluation.
Who to Trust in Multi-Robot Systems: Joint Robot and Object Hypothesis Trust Inference via Neural-Symbolic Cross-Validation The paper presents a neural-symbolic method for multi-robot systems to evaluate robot and object hypothesis trust purely via inter-robot observation consistency. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Covers multi-robot coordination, not visual inspection or manufacturing defects.
FDE-Bench: Evaluating End-to-End Delivery from Underspecified Real-World Business Requests The paper introduces a benchmark based on enterprise projects to evaluate agent performance on clarifying underspecified requirements and iterative optimization. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on capability benchmarking rather than safety, red-teaming, or guardrails.
GG-Nav: Goal-oriented Grounding Chain-of-Thought Elicits Reasoning in Navigation Foundation Models The paper proposes a structured chain-of-thought grounding method to track visual evidence across video frames for goal-oriented embodied navigation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a navigation framework rather than general LLM evaluation methodology.
GG-Nav: Goal-oriented Grounding Chain-of-Thought Elicits Reasoning in Navigation Foundation Models The paper proposes a structured chain-of-thought grounding method to track visual evidence across video frames for goal-oriented embodied navigation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on embodied indoor robot navigation, not industrial defect detection.
GG-Nav: Goal-oriented Grounding Chain-of-Thought Elicits Reasoning in Navigation Foundation Models The paper proposes a structured chain-of-thought grounding method to track visual evidence across video frames for goal-oriented embodied navigation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on navigation accuracy rather than safety guardrails or robustness.
Are Multimodal Benchmarks Really Useful? Item-Level Multimodal Benchmark Diagnosis via Structure-Response Co-Calibration The paper introduces a diagnosis framework to evaluate whether multimodal benchmark items genuinely require multi-image reasoning rather than unimodal shortcuts. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Analyzes multimodal benchmark quality rather than operational safety or guardrails.
Are Multimodal Benchmarks Really Useful? Item-Level Multimodal Benchmark Diagnosis via Structure-Response Co-Calibration The paper introduces a diagnosis framework to evaluate whether multimodal benchmark items genuinely require multi-image reasoning rather than unimodal shortcuts. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Examines dataset shortcuts in benchmarks rather than autonomous LLM agents.
A Self-Defining Operator for Distributed Applications The paper presents an autonomous agent architecture that distills distributed system incident resolutions into reusable detectors and operational playbooks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on an operational troubleshooting agent rather than benchmark design.
Active Neural Matter: A Finite-Window Response Framework for Physical Systems and Distributed AI Workflows The paper introduces a finite-window theoretical framework to determine what historical state information dynamical systems and distributed AI workflows must retain. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Deals with abstract dynamical systems rather than model compression or efficient serving.
Active Neural Matter: A Finite-Window Response Framework for Physical Systems and Distributed AI Workflows The paper introduces a finite-window theoretical framework to determine what historical state information dynamical systems and distributed AI workflows must retain. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Theoretical physics and dynamical systems framework, not language model agents.
Active Neural Matter: A Finite-Window Response Framework for Physical Systems and Distributed AI Workflows The paper introduces a finite-window theoretical framework to determine what historical state information dynamical systems and distributed AI workflows must retain. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Presents physics-inspired response bounds, not LLM deployment safety.
Active Neural Matter: A Finite-Window Response Framework for Physical Systems and Distributed AI Workflows The paper introduces a finite-window theoretical framework to determine what historical state information dynamical systems and distributed AI workflows must retain. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Investigates finite-window state response theory rather than LLM benchmarks.
LLM-based User-Product Relevance Evaluation for Dynamic Product Advertising The paper introduces a large language model-based metric for offline evaluation of user-product relevance in dynamic product advertising retrieval. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on advertising relevance evaluation rather than deployment safety or robustness.
LLM-based User-Product Relevance Evaluation for Dynamic Product Advertising The paper introduces a large language model-based metric for offline evaluation of user-product relevance in dynamic product advertising retrieval. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Uses LLM evaluation prompting rather than multi-step tool-using agents.
LASER: Defeasible Certificate Search for Long-Range Protocol Dependencies The paper presents a certificate-based search method paired with a symbolic checker to resolve complex, distributed dependencies in protocol specifications. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses formal specification checking rather than LLM deployment safety.
LASER: Defeasible Certificate Search for Long-Range Protocol Dependencies The paper presents a certificate-based search method paired with a symbolic checker to resolve complex, distributed dependencies in protocol specifications. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on proof-carrying protocol dependency search rather than LLM agents.
LASER: Defeasible Certificate Search for Long-Range Protocol Dependencies The paper presents a certificate-based search method paired with a symbolic checker to resolve complex, distributed dependencies in protocol specifications. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a formal verification search method rather than evaluating LLMs.
PsyMemBehav: Psychology-Informed and Memory-Grounded Two-Level Identity-to-Action Reasoning for Social-Media Behavior Simulation The paper develops a dataset and framework to simulate realistic user social media actions using psychological profiles and retrieved interaction histories. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not address deployment safety, red-teaming, jailbreaks, or model guardrails.
Learning after Execution with Action-Specific Credit The paper proposes a reinforcement learning method that assigns distinct credit to initial code generation and subsequent post-execution correction steps. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Examines learning algorithms rather than deployment safety, guardrails, or hallucination mitigation.
Learning after Execution with Action-Specific Credit The paper proposes a reinforcement learning method that assigns distinct credit to initial code generation and subsequent post-execution correction steps. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on reinforcement learning credit assignment, not benchmark evaluation methodologies.
CB-Agent: Concept-Based In-Context Learning for Sequential Action Selection The paper presents an in-context learning method that extracts compact concept vectors from demonstrations to guide sequential action choices for agents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on action selection accuracy rather than safety risks or alignment.
CB-Agent: Concept-Based In-Context Learning for Sequential Action Selection The paper presents an in-context learning method that extracts compact concept vectors from demonstrations to guide sequential action choices for agents. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an in-context learning agent method rather than benchmark evaluation.
Iterate on an LLM, Serve on an SLM The paper proposes prototyping agent workflows with large language models and distilling converged live production interactions into smaller models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on cost-effective distillation rather than AI safety guardrails or robustness.
Iterate on an LLM, Serve on an SLM The paper proposes prototyping agent workflows with large language models and distilling converged live production interactions into smaller models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on deployment workflows rather than benchmarking or evaluation metrics.
Decoupling Knowledge Forgetting and Refusal Learning in MLLM Unlearning The paper proposes a distillation framework to remove specific multimodal knowledge without causing hallucinations or leaving residual queryable information. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on knowledge erasure rather than model compression or inference efficiency.
Decoupling Knowledge Forgetting and Refusal Learning in MLLM Unlearning The paper proposes a distillation framework to remove specific multimodal knowledge without causing hallucinations or leaving residual queryable information. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on machine unlearning rather than multi-step agent planning or tool use.
Decoupling Knowledge Forgetting and Refusal Learning in MLLM Unlearning The paper proposes a distillation framework to remove specific multimodal knowledge without causing hallucinations or leaving residual queryable information. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Does not address code assistants, programming benchmarks, or software repair.
Decoupling Knowledge Forgetting and Refusal Learning in MLLM Unlearning The paper proposes a distillation framework to remove specific multimodal knowledge without causing hallucinations or leaving residual queryable information. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces an unlearning framework rather than general LLM evaluation benchmarks.
Decoupling Knowledge Forgetting and Refusal Learning in MLLM Unlearning The paper proposes a distillation framework to remove specific multimodal knowledge without causing hallucinations or leaving residual queryable information. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Studies model unlearning rather than retrieval-augmented generation systems.

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