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
Scaling Discovery Requires Forgetting Shows that periodically clearing agent search history and restarting from the top candidate prevents automated research agents from plateauing. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model compression or efficient inference.
Scaling Discovery Requires Forgetting Shows that periodically clearing agent search history and restarting from the top candidate prevents automated research agents from plateauing. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on agent search strategies rather than benchmark design.
Scaling Discovery Requires Forgetting Shows that periodically clearing agent search history and restarting from the top candidate prevents automated research agents from plateauing. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses search optimization rather than deployment safety risks.
Scaling Discovery Requires Forgetting Shows that periodically clearing agent search history and restarting from the top candidate prevents automated research agents from plateauing. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Focuses on search state forgetting, not RAG.
Recovering Reliable Supervision via Critique-Guided On-Policy Distillation Uses teacher critiques on incorrect student rollouts to improve on-policy distillation and recover supervision during mathematical reasoning tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes a training method rather than an evaluation framework.
Recovering Reliable Supervision via Critique-Guided On-Policy Distillation Uses teacher critiques on incorrect student rollouts to improve on-policy distillation and recover supervision during mathematical reasoning tasks. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on distillation algorithms for reasoning, not agent workflows.
Recovering Reliable Supervision via Critique-Guided On-Policy Distillation Uses teacher critiques on incorrect student rollouts to improve on-policy distillation and recover supervision during mathematical reasoning tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses reasoning distillation rather than deployment safety or guardrails.
One Task, Many Interfaces: Simple Cross-Interface Consistency Training Improves LLM Agent Robustness Introduces a self-supervised consistency loss across varied tool interfaces to make language model agents robust against notation and naming changes. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Does not involve multimodal inputs like vision or audio.
One Task, Many Interfaces: Simple Cross-Interface Consistency Training Improves LLM Agent Robustness Introduces a self-supervised consistency loss across varied tool interfaces to make language model agents robust against notation and naming changes. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes a training method, not a new evaluation benchmark.
One Task, Many Interfaces: Simple Cross-Interface Consistency Training Improves LLM Agent Robustness Introduces a self-supervised consistency loss across varied tool interfaces to make language model agents robust against notation and naming changes. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Does not focus on software engineering or code generation.
One Task, Many Interfaces: Simple Cross-Interface Consistency Training Improves LLM Agent Robustness Introduces a self-supervised consistency loss across varied tool interfaces to make language model agents robust against notation and naming changes. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not focus on model compression, quantization, or distillation.
One Task, Many Interfaces: Simple Cross-Interface Consistency Training Improves LLM Agent Robustness Introduces a self-supervised consistency loss across varied tool interfaces to make language model agents robust against notation and naming changes. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Focuses on agent interface robustness, not external document retrieval.
Player-1: Learning to Play from Synthetic Experience Develops a post-training framework that trains computer-use agents for video games using synthetic interactive experiences and an expanded action space. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on game agent reinforcement learning, not AI safety or guardrails.
Player-1: Learning to Play from Synthetic Experience Develops a post-training framework that trains computer-use agents for video games using synthetic interactive experiences and an expanded action space. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes a training framework and environment, not an LLM evaluation benchmark.
Player-1: Learning to Play from Synthetic Experience Develops a post-training framework that trains computer-use agents for video games using synthetic interactive experiences and an expanded action space. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on digital game control, not industrial vision or manufacturing inspection.
Continuum Attention Replaces discrete attention lookup in transformers with integration over a continuous memory curve to improve validation likelihood. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Proposes a novel attention operator, not multi-step autonomous agents.
Continuum Attention Replaces discrete attention lookup in transformers with integration over a continuous memory curve to improve validation likelihood. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Proposes a continuous attention operator without focusing on efficiency or compression.
Continuum Attention Replaces discrete attention lookup in transformers with integration over a continuous memory curve to improve validation likelihood. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces a continuous attention mechanism, not an LLM evaluation framework.
Continuum Attention Replaces discrete attention lookup in transformers with integration over a continuous memory curve to improve validation likelihood. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on core attention layer math, not AI safety or guardrails.
Price of Anarchy of First-Price Auctions in Agentic Markets Analyzes the price of anarchy and economic efficiency of first-price auctions where AI agents bid to serve user prompts. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on auction game theory rather than AI deployment safety risks.
Price of Anarchy of First-Price Auctions in Agentic Markets Analyzes the price of anarchy and economic efficiency of first-price auctions where AI agents bid to serve user prompts. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Analyzes auction economics rather than model compression or efficient inference.
Price of Anarchy of First-Price Auctions in Agentic Markets Analyzes the price of anarchy and economic efficiency of first-price auctions where AI agents bid to serve user prompts. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Theoretical analysis of agent auctions, not an empirical LLM benchmark.
Your Transformer Is Secretly Elastic Presents a retraining-free method to adapt transformer sizes dynamically across diverse computational budgets using joint soft pruning of components. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Proposes a transformer pruning technique, not multi-step agent systems.
Your Transformer Is Secretly Elastic Presents a retraining-free method to adapt transformer sizes dynamically across diverse computational budgets using joint soft pruning of components. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on dynamic neural pruning, not AI safety or guardrails.
Your Transformer Is Secretly Elastic Presents a retraining-free method to adapt transformer sizes dynamically across diverse computational budgets using joint soft pruning of components. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on dynamic model pruning, not an LLM benchmark.
Co-Evolving Acting and Forecasting for Language Agents Introduces multi-step action forecasting targets alongside actor rollouts to train language agents more effectively from intermediate trajectory steps. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on reinforcement learning efficiency, not small models or compression.
Co-Evolving Acting and Forecasting for Language Agents Introduces multi-step action forecasting targets alongside actor rollouts to train language agents more effectively from intermediate trajectory steps. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on agent policy learning, not AI safety or hallucination prevention.
Co-Evolving Acting and Forecasting for Language Agents Introduces multi-step action forecasting targets alongside actor rollouts to train language agents more effectively from intermediate trajectory steps. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes an agent training method rather than an evaluation benchmark.
Hierarchical Residual Policy Learning for Real-World Mobile Manipulation with Sparse Human Guidance Proposes a hierarchical residual policy learning framework to fine-tune mobile robot manipulation policies with decoupled base and arm adjustments. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Pertains to robotics control policies, not AI deployment safety or jailbreaks.
Hierarchical Residual Policy Learning for Real-World Mobile Manipulation with Sparse Human Guidance Proposes a hierarchical residual policy learning framework to fine-tune mobile robot manipulation policies with decoupled base and arm adjustments. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents a policy fine-tuning algorithm, not an LLM benchmark or evaluation.
Hierarchical Residual Policy Learning for Real-World Mobile Manipulation with Sparse Human Guidance Proposes a hierarchical residual policy learning framework to fine-tune mobile robot manipulation policies with decoupled base and arm adjustments. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on robotic reinforcement learning, not LLM agents or tool use.
Hierarchical Residual Policy Learning for Real-World Mobile Manipulation with Sparse Human Guidance Proposes a hierarchical residual policy learning framework to fine-tune mobile robot manipulation policies with decoupled base and arm adjustments. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Addresses mobile robot manipulation, not industrial defect detection or visual quality control.
Hierarchical Residual Policy Learning for Real-World Mobile Manipulation with Sparse Human Guidance Proposes a hierarchical residual policy learning framework to fine-tune mobile robot manipulation policies with decoupled base and arm adjustments. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on sample-efficient robotic RL, not LLM quantization or compression.
Modeling and Understanding Neural Weights of Humanoid Policies with Diffusion Generatively models humanoid robot control policy weights using a latent diffusion framework combined with a linear autoencoder and Diffusion Transformer. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on humanoid locomotion policies, not industrial defect inspection.
Modeling and Understanding Neural Weights of Humanoid Policies with Diffusion Generatively models humanoid robot control policy weights using a latent diffusion framework combined with a linear autoencoder and Diffusion Transformer. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Studies generative modeling of robot policy weights, not LLM benchmarks.
Modeling and Understanding Neural Weights of Humanoid Policies with Diffusion Generatively models humanoid robot control policy weights using a latent diffusion framework combined with a linear autoencoder and Diffusion Transformer. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant About humanoid robot control policies, not LLM safety or guardrails.
Modeling and Understanding Neural Weights of Humanoid Policies with Diffusion Generatively models humanoid robot control policy weights using a latent diffusion framework combined with a linear autoencoder and Diffusion Transformer. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Generates policy weights using diffusion, not model compression or efficient serving.
Modeling and Understanding Neural Weights of Humanoid Policies with Diffusion Generatively models humanoid robot control policy weights using a latent diffusion framework combined with a linear autoencoder and Diffusion Transformer. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on humanoid robotics policy weights, not LLM-based planning agents.
Laterally Coupled DFB Lasers With Self-Aligned Metal Surface Grating by Holographic Lithography This paper reports the fabrication and continuous-wave optical performance of laterally coupled distributed feedback semiconductor lasers. Peer-reviewedOlder paper (2008) Older paper (2008) — discussed this week	AI safety and reliability in deployment via ieee · 15 Oct, 06:19	Not relevant Semiconductor laser physics paper, completely unrelated to AI safety.
Buffer-Free HEMT With Ultra-Thin AlN Back Barrier and GaN Channel Suppressing Drain Lag and Quiescent Drain Current Drift This paper evaluates a buffer-free transistor structure designed to suppress current drift and lag in high-power electronics. Peer-reviewedOlder paper (2026) Older paper (2026) — discussed this week	AI safety and reliability in deployment via ieee · 15 Oct, 06:19	Not relevant Hardware semiconductor engineering paper, completely unrelated to AI safety.
Reliability study of AlGaN/GaN/Si HEMTs using Capacitance-Voltage measurements This paper surveys capacitance-voltage measurement techniques to evaluate defect states and reliability in gallium nitride transistor devices. Peer-reviewedOlder paper (2025) Older paper (2025) — discussed this week	AI safety and reliability in deployment via ieee · 15 Oct, 06:19	Not relevant About semiconductor transistor reliability, not AI system safety or reliability.
On the Evaluation of Large Language Models in Unit Test Generation This paper presents an empirical study evaluating open-source language models for automated unit test generation in software development. Peer-reviewedOlder paper (2024) Older paper (2024) — discussed this week	AI safety and reliability in deployment via acm · 15 Oct, 06:19	Not relevant Evaluates unit test generation rather than operational AI deployment safety.
LayoutDM: Discrete Diffusion Model for Controllable Layout Generation This paper proposes a discrete diffusion model for controllable visual layout generation under structural element constraints. Peer-reviewedOlder paper (2023) Older paper (2023) — discussed this week	Computer vision for industrial applications via ieee · 15 Oct, 06:19	Not relevant Focuses on discrete layout synthesis, not industrial defect detection.
LayoutDM: Discrete Diffusion Model for Controllable Layout Generation This paper proposes a discrete diffusion model for controllable visual layout generation under structural element constraints. Peer-reviewedOlder paper (2023) Older paper (2023) — discussed this week	Retrieval-augmented generation (RAG) via ieee · 15 Oct, 06:19	Not relevant Covers bounding box generation, not retrieval-augmented generation.
LayoutDM: Discrete Diffusion Model for Controllable Layout Generation This paper proposes a discrete diffusion model for controllable visual layout generation under structural element constraints. Peer-reviewedOlder paper (2023) Older paper (2023) — discussed this week	Multimodal models via ieee · 15 Oct, 06:19	Not relevant Generates layout bounding boxes rather than multimodal reasoning across vision and text.
Automated Non-Functional Requirements Generation in Software Engineering with Large Language Models: A Comparative Study This paper evaluates the ability of language models to generate non-functional requirements from functional software specifications. Peer-reviewedOlder paper (2025) Older paper (2025) — discussed this week	AI agents via ieee · 15 Oct, 06:19	Not relevant Uses LLM prompting pipelines, not multi-step autonomous planning agents.
Automated Non-Functional Requirements Generation in Software Engineering with Large Language Models: A Comparative Study This paper evaluates the ability of language models to generate non-functional requirements from functional software specifications. Peer-reviewedOlder paper (2025) Older paper (2025) — discussed this week	LLM evaluation and benchmarks via ieee · 15 Oct, 06:19	Not relevant Empirical study of requirements generation rather than an LLM evaluation benchmark.
Automated Non-Functional Requirements Generation in Software Engineering with Large Language Models: A Comparative Study This paper evaluates the ability of language models to generate non-functional requirements from functional software specifications. Peer-reviewedOlder paper (2025) Older paper (2025) — discussed this week	AI safety and reliability in deployment via ieee · 15 Oct, 06:19	Not relevant Addresses software engineering requirements rather than operational AI safety and robustness.
Automated Non-Functional Requirements Generation in Software Engineering with Large Language Models: A Comparative Study This paper evaluates the ability of language models to generate non-functional requirements from functional software specifications. Peer-reviewedOlder paper (2025) Older paper (2025) — discussed this week	Retrieval-augmented generation (RAG) via ieee · 15 Oct, 06:19	Not relevant About non-functional requirement extraction, not retrieval-augmented generation.
MAGE: A Multi-Agent Engine for Automated RTL Code Generation This paper introduces an open-source multi-agent system designed to generate, verify, and debug hardware description code. Peer-reviewedOlder paper (2025) Older paper (2025) — discussed this week	Retrieval-augmented generation (RAG) via ieee · 15 Oct, 06:19	Not relevant Focuses on Verilog code generation, not retrieval-augmented generation.
Video-Bench: Human-Aligned Video Generation Benchmark This paper introduces an evaluation benchmark using multimodal models to assess generated videos across human preference dimensions. Peer-reviewedOlder paper (2025) Older paper (2025) — discussed this week	Retrieval-augmented generation (RAG) via ieee · 15 Oct, 06:19	Not relevant About video generation benchmarking, not retrieval-augmented generation systems.
Video-Bench: Human-Aligned Video Generation Benchmark This paper introduces an evaluation benchmark using multimodal models to assess generated videos across human preference dimensions. Peer-reviewedOlder paper (2025) Older paper (2025) — discussed this week	AI safety and reliability in deployment via ieee · 15 Oct, 06:19	Not relevant Focuses on video quality benchmarking rather than AI safety or guardrails.
Video-Bench: Human-Aligned Video Generation Benchmark This paper introduces an evaluation benchmark using multimodal models to assess generated videos across human preference dimensions. Peer-reviewedOlder paper (2025) Older paper (2025) — discussed this week	Computer vision for industrial applications via ieee · 15 Oct, 06:19	Not relevant Targets generative video evaluation, not industrial visual inspection or defect detection.
Video-Bench: Human-Aligned Video Generation Benchmark This paper introduces an evaluation benchmark using multimodal models to assess generated videos across human preference dimensions. Peer-reviewedOlder paper (2025) Older paper (2025) — discussed this week	AI agents via ieee · 15 Oct, 06:19	Not relevant Evaluates video generation models, not multi-step tool-using agents.
REvolution: An Evolutionary Framework for RTL Generation driven by Large Language Models This paper combines evolutionary computation with language models to optimize register-transfer level hardware code for correctness and efficiency. Peer-reviewedOlder paper (2025) Older paper (2025) — discussed this week	AI agents via ieee · 15 Oct, 06:19	Not relevant Applies evolutionary optimization to LLM prompts, not multi-step agent systems.
REvolution: An Evolutionary Framework for RTL Generation driven by Large Language Models This paper combines evolutionary computation with language models to optimize register-transfer level hardware code for correctness and efficiency. Peer-reviewedOlder paper (2025) Older paper (2025) — discussed this week	AI safety and reliability in deployment via ieee · 15 Oct, 06:19	Not relevant Focuses on hardware code synthesis, not deployment safety or reliability.
REvolution: An Evolutionary Framework for RTL Generation driven by Large Language Models This paper combines evolutionary computation with language models to optimize register-transfer level hardware code for correctness and efficiency. Peer-reviewedOlder paper (2025) Older paper (2025) — discussed this week	LLM evaluation and benchmarks via ieee · 15 Oct, 06:19	Not relevant Develops a code generation framework rather than a general evaluation benchmark.
REvolution: An Evolutionary Framework for RTL Generation driven by Large Language Models This paper combines evolutionary computation with language models to optimize register-transfer level hardware code for correctness and efficiency. Peer-reviewedOlder paper (2025) Older paper (2025) — discussed this week	Retrieval-augmented generation (RAG) via ieee · 15 Oct, 06:19	Not relevant About RTL code generation, not retrieval-augmented generation.
A Survey of Synthetic Data Generation for Machine Learning This paper surveys synthetic data generation and data augmentation techniques for machine learning with an emphasis on healthcare. Peer-reviewedOlder paper (2021) Older paper (2021) — discussed this week	Retrieval-augmented generation (RAG) via ieee · 15 Oct, 06:19	Not relevant Focuses on synthetic data generation, not retrieval-augmented generation.
Specifying the Delegated-Autonomy Boundary: Requirements Engineering for Agentic AI This paper introduces specification templates to define operational boundaries, authority, and oversight for autonomous software agents. Peer-reviewedOlder paper (2026) Older paper (2026) — discussed this week	LLM evaluation and benchmarks via acm · 15 Oct, 06:19	Not relevant Proposes requirements engineering frameworks rather than LLM evaluation benchmarks.

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