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
Probabilistic Multi-Source Data Selection via Learnable Categorical Priors: Variance Reduction and Guaranteed Derandomization This paper introduces a probabilistic relaxation method with learnable categorical priors for bilevel multi-source data selection. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on dataset selection algorithms rather than model compression or inference efficiency.
Probabilistic Multi-Source Data Selection via Learnable Categorical Priors: Variance Reduction and Guaranteed Derandomization This paper introduces a probabilistic relaxation method with learnable categorical priors for bilevel multi-source data selection. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Deals with bilevel optimization rather than real-world AI deployment safety.
Probabilistic Multi-Source Data Selection via Learnable Categorical Priors: Variance Reduction and Guaranteed Derandomization This paper introduces a probabilistic relaxation method with learnable categorical priors for bilevel multi-source data selection. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on bilevel combinatorial data selection optimization, not LLM agents.
Counterfactual Rollouts for Offline Contrastive Goal-Conditioned Reinforcement Learning This paper proposes a causal contrastive reinforcement learning method using counterfactual rollouts to handle unobserved confounding in offline demonstrations. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Deals with RL counterfactual transitions rather than model compression or efficient serving.
Counterfactual Rollouts for Offline Contrastive Goal-Conditioned Reinforcement Learning This paper proposes a causal contrastive reinforcement learning method using counterfactual rollouts to handle unobserved confounding in offline demonstrations. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses confounded RL transitions rather than LLM safety, guardrails, or robustness.
Counterfactual Rollouts for Offline Contrastive Goal-Conditioned Reinforcement Learning This paper proposes a causal contrastive reinforcement learning method using counterfactual rollouts to handle unobserved confounding in offline demonstrations. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on offline goal-conditioned reinforcement learning theory, not LLM agents.
Counterfactual Rollouts for Offline Contrastive Goal-Conditioned Reinforcement Learning This paper proposes a causal contrastive reinforcement learning method using counterfactual rollouts to handle unobserved confounding in offline demonstrations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Theoretical RL algorithmic formulation without LLM evaluation or benchmark focus.
MCPDisCo: Seesaw-Mitigated Tuning for MCP-Native Agents This paper presents a reinforcement learning framework to train language model agents across tools and servers in the Model Context Protocol ecosystem. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a training framework rather than a general LLM evaluation benchmark.
MCPDisCo: Seesaw-Mitigated Tuning for MCP-Native Agents This paper presents a reinforcement learning framework to train language model agents across tools and servers in the Model Context Protocol ecosystem. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on tool execution learning rather than deployment guardrails or safety risks.
What Survives When Activation Steering Is Used as Memory This paper evaluates the capabilities and limitations of using activation steering vectors as an agent memory mechanism. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Studies parametric activation shifts instead of external document retrieval or RAG architectures.
What Survives When Activation Steering Is Used as Memory This paper evaluates the capabilities and limitations of using activation steering vectors as an agent memory mechanism. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Studies memory storage in language models, not code generation or program repair.
What Survives When Activation Steering Is Used as Memory This paper evaluates the capabilities and limitations of using activation steering vectors as an agent memory mechanism. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Studies text-based activation steering without multimodal components.
What Survives When Activation Steering Is Used as Memory This paper evaluates the capabilities and limitations of using activation steering vectors as an agent memory mechanism. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Examines memory storage mechanisms rather than general LLM evaluation benchmarks.
What Survives When Activation Steering Is Used as Memory This paper evaluates the capabilities and limitations of using activation steering vectors as an agent memory mechanism. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Analyzes vector memory retention rather than model compression or inference acceleration.
AlphaRLM: Aligning Language Models for Literature-Grounded Symbolic Factor Discovery This paper trains language models using reinforcement learning to construct executable financial trading formulas grounded in research literature. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on RL formulation for factor mining rather than model compression or efficient inference.
AlphaRLM: Aligning Language Models for Literature-Grounded Symbolic Factor Discovery This paper trains language models using reinforcement learning to construct executable financial trading formulas grounded in research literature. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on alpha discovery methodology rather than LLM evaluation benchmarks.
AlphaRLM: Aligning Language Models for Literature-Grounded Symbolic Factor Discovery This paper trains language models using reinforcement learning to construct executable financial trading formulas grounded in research literature. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Processes text literature and symbolic expressions without multimodal inputs.
AlphaRLM: Aligning Language Models for Literature-Grounded Symbolic Factor Discovery This paper trains language models using reinforcement learning to construct executable financial trading formulas grounded in research literature. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Uses literature as semantic guidance in training rather than real-time retrieval-augmented generation.
AlphaRLM: Aligning Language Models for Literature-Grounded Symbolic Factor Discovery This paper trains language models using reinforcement learning to construct executable financial trading formulas grounded in research literature. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses financial factor generation rather than AI safety or reliability risks.
Humans vs. Machines: A Comparison With AI Agents in Software Reverse Engineering This paper benchmarks language model agents against skilled humans on binary software reverse engineering tasks using interactive tools. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Analyzes reverse engineering capability rather than safety guardrails or alignment.
SkillHorizon: Constructing Long-Horizon Agent Tasks through Skill Composition This paper introduces an evaluation benchmark that generates complex, long-horizon agent workflows by composing verifiable business skills. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on task horizon benchmarking rather than deployment safety or guardrails.
FleetPilot: Fine-Grained Steering Empowers Real-World LLM Personalization This paper proposes a steering framework that dynamically personalizes language model responses using low-overhead activation vectors. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an inference steering method rather than an evaluation methodology or benchmark.
FleetPilot: Fine-Grained Steering Empowers Real-World LLM Personalization This paper proposes a steering framework that dynamically personalizes language model responses using low-overhead activation vectors. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses personalization adaptation rather than deployment safety, jailbreaks, or hallucinations.
FleetPilot: Fine-Grained Steering Empowers Real-World LLM Personalization This paper proposes a steering framework that dynamically personalizes language model responses using low-overhead activation vectors. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on activation steering for user personalization, not agent planning or tool use.
Single-Sample Decoding of a Two-Layer Sound-Word Canvas Model for Thai Klon Pad This paper introduces a two-layer masked language model approach to generate constrained Thai poetry in a single decoding pass. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on Thai poetry generation rather than standard LLM evaluation frameworks.
Single-Sample Decoding of a Two-Layer Sound-Word Canvas Model for Thai Klon Pad This paper introduces a two-layer masked language model approach to generate constrained Thai poetry in a single decoding pass. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Presents a constrained masked language model for poetry, not an autonomous agent.
Single-Sample Decoding of a Two-Layer Sound-Word Canvas Model for Thai Klon Pad This paper introduces a two-layer masked language model approach to generate constrained Thai poetry in a single decoding pass. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Deals with poetic form enforcement rather than safety, red-teaming, or guardrails.
Fitting Every Hint Is Not Enough: Premature Floods in Recurrent Algorithmic Reasoners This paper investigates failure modes where recurrent algorithmic reasoning models jump ahead prematurely when generalizing to larger graphs. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Studies algorithmic reasoner generalization, not model compression or efficient serving.
Fitting Every Hint Is Not Enough: Premature Floods in Recurrent Algorithmic Reasoners This paper investigates failure modes where recurrent algorithmic reasoning models jump ahead prematurely when generalizing to larger graphs. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies recurrent graph reasoners, not LLM agent planning or tool use.
Fitting Every Hint Is Not Enough: Premature Floods in Recurrent Algorithmic Reasoners This paper investigates failure modes where recurrent algorithmic reasoning models jump ahead prematurely when generalizing to larger graphs. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on graph neural network generalization rather than LLM safety or deployment guardrails.
Fitting Every Hint Is Not Enough: Premature Floods in Recurrent Algorithmic Reasoners This paper investigates failure modes where recurrent algorithmic reasoning models jump ahead prematurely when generalizing to larger graphs. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Analyzes failure modes in graph neural networks, not LLM evaluation benchmarks.
Too Late to Prune: Generative AI, the Screen, and Team Creative Decisions This paper studies experimentally how the timing of generative AI access impacts team ideation and creative selection. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Investigates human organizational creativity rather than LLM benchmark design or evaluation.
Too Late to Prune: Generative AI, the Screen, and Team Creative Decisions This paper studies experimentally how the timing of generative AI access impacts team ideation and creative selection. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on team decision timing rather than technical AI safety or hallucination.
Too Late to Prune: Generative AI, the Screen, and Team Creative Decisions This paper studies experimentally how the timing of generative AI access impacts team ideation and creative selection. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies human team workflows with generative tools, not autonomous LLM agents.
Mixing Regularization for Average-Reward $Q$-Learning and $Q$-Planning This paper provides theoretical convergence guarantees for average-reward reinforcement learning algorithms via mixing policy regularization. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses abstract RL convergence rather than real-world AI deployment safety.
Mixing Regularization for Average-Reward $Q$-Learning and $Q$-Planning This paper provides theoretical convergence guarantees for average-reward reinforcement learning algorithms via mixing policy regularization. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Theoretical analysis of Q-learning, not language model agent systems.
Mixing Regularization for Average-Reward $Q$-Learning and $Q$-Planning This paper provides theoretical convergence guarantees for average-reward reinforcement learning algorithms via mixing policy regularization. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Deals with reinforcement learning optimization rates, not model efficiency or distillation.
Mixing Regularization for Average-Reward $Q$-Learning and $Q$-Planning This paper provides theoretical convergence guarantees for average-reward reinforcement learning algorithms via mixing policy regularization. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Provides convergence theory for reinforcement learning rather than LLM evaluation.
Certify the Decision, Not Every Score: Exact Top-k Selection from Few Evaluations This paper proposes an adaptive selection method that identifies the top candidates under uncertainty using minimal scenario evaluations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Addresses scenario evaluation algorithms, not LLM testing benchmarks or LLM-as-a-judge.
Certify the Decision, Not Every Score: Exact Top-k Selection from Few Evaluations This paper proposes an adaptive selection method that identifies the top candidates under uncertainty using minimal scenario evaluations. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on statistical selection guarantees rather than LLM safety or guardrails.
Certify the Decision, Not Every Score: Exact Top-k Selection from Few Evaluations This paper proposes an adaptive selection method that identifies the top candidates under uncertainty using minimal scenario evaluations. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Theoretical decision-making algorithm for candidate selection, not LLM agents.
Recurrent In-Context Learning for Tabular Foundation Models This paper introduces parameter-tied recurrent blocks to reduce model size in tabular foundation models without sacrificing performance. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies tabular foundation model architectures, not autonomous agents.
Recurrent In-Context Learning for Tabular Foundation Models This paper introduces parameter-tied recurrent blocks to reduce model size in tabular foundation models without sacrificing performance. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on parameter sharing efficiency rather than safety or reliability.
Recurrent In-Context Learning for Tabular Foundation Models This paper introduces parameter-tied recurrent blocks to reduce model size in tabular foundation models without sacrificing performance. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a model architecture rather than an evaluation framework or benchmark.
Can You Trust Your Preference Models? Stackelberg Games for Alignment This paper models preference model alignment as a Stackelberg game to identify comparisons where evaluators are prone to error. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on preference model alignment games, not LLM tool use or multi-agent planning.
On the Complexity Gap Between POMDPs and Multi-Environment POMDPs This paper maps the computational complexity landscape of robust decision-making in multi-environment partially observable environments. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Theoretical complexity analysis of POMDPs, not LLM agent workflows.
On the Complexity Gap Between POMDPs and Multi-Environment POMDPs This paper maps the computational complexity landscape of robust decision-making in multi-environment partially observable environments. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Pure theoretical analysis of robust reward maximization without deployment safety focus.
On the Complexity Gap Between POMDPs and Multi-Environment POMDPs This paper maps the computational complexity landscape of robust decision-making in multi-environment partially observable environments. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies computational complexity theory, not language model benchmarks.
UltraBERT: Beyond the Limit of BERT Pre-Training This paper analyzes pretraining factors such as masking ratios and token budgets to optimize bidirectional transformer encoders. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on pre-training BERT encoders, not LLM multi-step agents.
UltraBERT: Beyond the Limit of BERT Pre-Training This paper analyzes pretraining factors such as masking ratios and token budgets to optimize bidirectional transformer encoders. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses pre-training optimizations rather than deployment safety, robustness, or jailbreaks.
UltraBERT: Beyond the Limit of BERT Pre-Training This paper analyzes pretraining factors such as masking ratios and token budgets to optimize bidirectional transformer encoders. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Investigates pre-training recipes rather than benchmarking frameworks or evaluation methodology.
UltraBERT: Beyond the Limit of BERT Pre-Training This paper analyzes pretraining factors such as masking ratios and token budgets to optimize bidirectional transformer encoders. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses strictly on text-based bidirectional encoders, not multimodal models.
UltraBERT: Beyond the Limit of BERT Pre-Training This paper analyzes pretraining factors such as masking ratios and token budgets to optimize bidirectional transformer encoders. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Deals with general language encoder pre-training, not software engineering models.
Toward System One Clinical Intelligence: Parameter-Efficient Fine-Tuning and Counterfactual Explainability of Decision Foundation Models This paper fine-tunes a quantized decision model to classify diseases from patient symptom descriptions without free-form text generation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates on an existing benchmark rather than proposing new LLM evaluation methods.
Toward System One Clinical Intelligence: Parameter-Efficient Fine-Tuning and Counterfactual Explainability of Decision Foundation Models This paper fine-tunes a quantized decision model to classify diseases from patient symptom descriptions without free-form text generation. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Addresses clinical text classification, not code generation or software engineering.
Toward System One Clinical Intelligence: Parameter-Efficient Fine-Tuning and Counterfactual Explainability of Decision Foundation Models This paper fine-tunes a quantized decision model to classify diseases from patient symptom descriptions without free-form text generation. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Processes text-only clinical symptoms rather than multimodal data.
Toward System One Clinical Intelligence: Parameter-Efficient Fine-Tuning and Counterfactual Explainability of Decision Foundation Models This paper fine-tunes a quantized decision model to classify diseases from patient symptom descriptions without free-form text generation. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on classification without retrieval-augmented generation mechanisms.
Toward System One Clinical Intelligence: Parameter-Efficient Fine-Tuning and Counterfactual Explainability of Decision Foundation Models This paper fine-tunes a quantized decision model to classify diseases from patient symptom descriptions without free-form text generation. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Presents a classification model, not an agent planning or tool use system.
Toward System One Clinical Intelligence: Parameter-Efficient Fine-Tuning and Counterfactual Explainability of Decision Foundation Models This paper fine-tunes a quantized decision model to classify diseases from patient symptom descriptions without free-form text generation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Investigates explainability for a classifier rather than deployment safety guardrails or hallucinations.
InCLAD: A Continual Learning Benchmark for Industrial Visual Anomaly Detection This paper introduces a benchmark to evaluate continual learning in semi-supervised visual industrial anomaly detection across components. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not address language model deployment safety, jailbreaks, or guardrails.

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