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
Learning Proposition-Level Links from Sentence-Level Supervision for Neuro-Symbolic NLI The paper proposes a trainable proposition linker that learns atom-level semantic links from sentence-level supervision for neuro-symbolic inference. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Applies to natural language inference rather than software code.
Learning Proposition-Level Links from Sentence-Level Supervision for Neuro-Symbolic NLI The paper proposes a trainable proposition linker that learns atom-level semantic links from sentence-level supervision for neuro-symbolic inference. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Focuses on propositional linking in NLI, not agentic planning.
Learning Proposition-Level Links from Sentence-Level Supervision for Neuro-Symbolic NLI The paper proposes a trainable proposition linker that learns atom-level semantic links from sentence-level supervision for neuro-symbolic inference. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not study model pruning, quantization, or efficient serving.
Sanzaru: Benchmarking Protocol Formation under Epistemic & Communication Asymmetry The paper introduces a benchmark evaluating how multi-agent teams develop communication protocols under asymmetric information and restricted communication channels. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Focuses on cooperative puzzle communication, not software coding.
Sanzaru: Benchmarking Protocol Formation under Epistemic & Communication Asymmetry The paper introduces a benchmark evaluating how multi-agent teams develop communication protocols under asymmetric information and restricted communication channels. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Measures multi-agent coordination rather than safety red-teaming or guardrails.
Sanzaru: Benchmarking Protocol Formation under Epistemic & Communication Asymmetry The paper introduces a benchmark evaluating how multi-agent teams develop communication protocols under asymmetric information and restricted communication channels. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Does not involve external document retrieval or grounding systems.
Sanzaru: Benchmarking Protocol Formation under Epistemic & Communication Asymmetry The paper introduces a benchmark evaluating how multi-agent teams develop communication protocols under asymmetric information and restricted communication channels. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not examine model compression or inference efficiency.
Sanzaru: Benchmarking Protocol Formation under Epistemic & Communication Asymmetry The paper introduces a benchmark evaluating how multi-agent teams develop communication protocols under asymmetric information and restricted communication channels. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on communicative interaction rather than multimodal vision-language models.
Active Learning for Finding a Needle in a Haystack The paper evaluates whether active learning strategies can overcome initial selection biases when identifying rare document classes in large text corpora. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Uses text document corpora without multimodal integration.
Active Learning for Finding a Needle in a Haystack The paper evaluates whether active learning strategies can overcome initial selection biases when identifying rare document classes in large text corpora. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on data labeling strategies, not efficient model architectures.
Active Learning for Finding a Needle in a Haystack The paper evaluates whether active learning strategies can overcome initial selection biases when identifying rare document classes in large text corpora. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Evaluates active learning algorithms rather than LLM benchmark design.
Active Learning for Finding a Needle in a Haystack The paper evaluates whether active learning strategies can overcome initial selection biases when identifying rare document classes in large text corpora. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Evaluates document classification datasets, not software engineering tasks.
Active Learning for Finding a Needle in a Haystack The paper evaluates whether active learning strategies can overcome initial selection biases when identifying rare document classes in large text corpora. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Focuses on classification active learning, not retrieval-augmented generation.
Active Learning for Finding a Needle in a Haystack The paper evaluates whether active learning strategies can overcome initial selection biases when identifying rare document classes in large text corpora. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Studies active learning sampling algorithms, not LLM agent systems.
Active Learning for Finding a Needle in a Haystack The paper evaluates whether active learning strategies can overcome initial selection biases when identifying rare document classes in large text corpora. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses dataset active learning rather than AI deployment safety risks.
Word-Aligned Prosody Reflects Speaker Differences More Than Depression or PTSD Severity The paper investigates whether word-aligned prosodic speech measurements reflect speaker identity traits more strongly than depression or PTSD severity. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Studies clinical speech prosody metrics, not LLM agents.
Word-Aligned Prosody Reflects Speaker Differences More Than Depression or PTSD Severity The paper investigates whether word-aligned prosodic speech measurements reflect speaker identity traits more strongly than depression or PTSD severity. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Does not explore document retrieval or RAG systems.
Word-Aligned Prosody Reflects Speaker Differences More Than Depression or PTSD Severity The paper investigates whether word-aligned prosodic speech measurements reflect speaker identity traits more strongly than depression or PTSD severity. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on clinical depression detection rather than AI deployment safety.
Word-Aligned Prosody Reflects Speaker Differences More Than Depression or PTSD Severity The paper investigates whether word-aligned prosodic speech measurements reflect speaker identity traits more strongly than depression or PTSD severity. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Investigates speech prosody rather than software code generation.
Word-Aligned Prosody Reflects Speaker Differences More Than Depression or PTSD Severity The paper investigates whether word-aligned prosodic speech measurements reflect speaker identity traits more strongly than depression or PTSD severity. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not focus on model compression, pruning, or efficient inference.
Word-Aligned Prosody Reflects Speaker Differences More Than Depression or PTSD Severity The paper investigates whether word-aligned prosodic speech measurements reflect speaker identity traits more strongly than depression or PTSD severity. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Evaluates acoustic biomarkers rather than language model benchmarks.
Word-Aligned Prosody Reflects Speaker Differences More Than Depression or PTSD Severity The paper investigates whether word-aligned prosodic speech measurements reflect speaker identity traits more strongly than depression or PTSD severity. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Studies acoustic prosody analysis rather than multimodal vision-language models.
SAFE-KV: Causal Suffix Repair for Behavior-Preserving Non-Prefix KV Cache Reuse in RAG The paper presents a repair method that re-encodes causal suffixes to enable safe non-prefix KV-cache reuse in retrieval-augmented generation. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on text document KV caches without multimodal inputs.
SAFE-KV: Causal Suffix Repair for Behavior-Preserving Non-Prefix KV Cache Reuse in RAG The paper presents a repair method that re-encodes causal suffixes to enable safe non-prefix KV-cache reuse in retrieval-augmented generation. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Optimizes attention KV cache serving, not software engineering.
SAFE-KV: Causal Suffix Repair for Behavior-Preserving Non-Prefix KV Cache Reuse in RAG The paper presents a repair method that re-encodes causal suffixes to enable safe non-prefix KV-cache reuse in retrieval-augmented generation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Optimizes serving performance rather than AI safety or guardrails.
SAFE-KV: Causal Suffix Repair for Behavior-Preserving Non-Prefix KV Cache Reuse in RAG The paper presents a repair method that re-encodes causal suffixes to enable safe non-prefix KV-cache reuse in retrieval-augmented generation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Introduces a cache repair technique rather than an evaluation benchmark.
SAFE-KV: Causal Suffix Repair for Behavior-Preserving Non-Prefix KV Cache Reuse in RAG The paper presents a repair method that re-encodes causal suffixes to enable safe non-prefix KV-cache reuse in retrieval-augmented generation. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Focuses on KV cache state reuse rather than multi-step agents.
Do LLMs differentiate linguistic traits internally?: A layer-wise analysis The paper conducts layer-wise representational analysis to determine how language models internally distinguish conventional meanings from idiomatic traits. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies layer representations rather than compression or efficient serving.
Do LLMs differentiate linguistic traits internally?: A layer-wise analysis The paper conducts layer-wise representational analysis to determine how language models internally distinguish conventional meanings from idiomatic traits. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on internal linguistic representation rather than deployment safety.
Do LLMs differentiate linguistic traits internally?: A layer-wise analysis The paper conducts layer-wise representational analysis to determine how language models internally distinguish conventional meanings from idiomatic traits. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Does not evaluate retrieval-augmented generation or knowledge retrieval.
Do LLMs differentiate linguistic traits internally?: A layer-wise analysis The paper conducts layer-wise representational analysis to determine how language models internally distinguish conventional meanings from idiomatic traits. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Performs representational layer analysis, not agentic planning or tools.
Do LLMs differentiate linguistic traits internally?: A layer-wise analysis The paper conducts layer-wise representational analysis to determine how language models internally distinguish conventional meanings from idiomatic traits. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Analyzes linguistic trait embeddings, not code generation.
Do LLMs differentiate linguistic traits internally?: A layer-wise analysis The paper conducts layer-wise representational analysis to determine how language models internally distinguish conventional meanings from idiomatic traits. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Examines internal interpretability probing rather than LLM benchmarking.
Do LLMs differentiate linguistic traits internally?: A layer-wise analysis The paper conducts layer-wise representational analysis to determine how language models internally distinguish conventional meanings from idiomatic traits. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Analyzes text models without image, audio, or video modalities.
Do correct answers have stable latent knowledge support? The paper proposes stability metrics to measure whether correct outputs in language models are backed by stable internal knowledge representations. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Evaluates text-based models across factual queries without multimodal inputs.
Do correct answers have stable latent knowledge support? The paper proposes stability metrics to measure whether correct outputs in language models are backed by stable internal knowledge representations. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Investigates latent state representations, not multi-step agent frameworks.
Do correct answers have stable latent knowledge support? The paper proposes stability metrics to measure whether correct outputs in language models are backed by stable internal knowledge representations. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Examines factual and math stability, not software code generation.
Do correct answers have stable latent knowledge support? The paper proposes stability metrics to measure whether correct outputs in language models are backed by stable internal knowledge representations. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Studies internal parametric representations rather than retrieval-augmented generation.
Do correct answers have stable latent knowledge support? The paper proposes stability metrics to measure whether correct outputs in language models are backed by stable internal knowledge representations. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not address model pruning, distillation, or serving efficiency.
From Candidate Tokens to Final Outcomes: Decoding Relative Long-Horizon Candidate Value from Reasoning Model Representations The paper constructs a dataset to show that relative candidate token value in multi-step reasoning is linearly decodable from model representations. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Does not study retrieval-augmented generation or external document grounding.
From Candidate Tokens to Final Outcomes: Decoding Relative Long-Horizon Candidate Value from Reasoning Model Representations The paper constructs a dataset to show that relative candidate token value in multi-step reasoning is linearly decodable from model representations. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Analyzes internal representations in reasoning models, not agent systems.
From Candidate Tokens to Final Outcomes: Decoding Relative Long-Horizon Candidate Value from Reasoning Model Representations The paper constructs a dataset to show that relative candidate token value in multi-step reasoning is linearly decodable from model representations. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Studies mathematical reasoning tokens rather than software development.
From Candidate Tokens to Final Outcomes: Decoding Relative Long-Horizon Candidate Value from Reasoning Model Representations The paper constructs a dataset to show that relative candidate token value in multi-step reasoning is linearly decodable from model representations. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not study model compression, distillation, or inference optimization.
From Candidate Tokens to Final Outcomes: Decoding Relative Long-Horizon Candidate Value from Reasoning Model Representations The paper constructs a dataset to show that relative candidate token value in multi-step reasoning is linearly decodable from model representations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Investigates internal representation probing rather than evaluation benchmark integrity.
From Candidate Tokens to Final Outcomes: Decoding Relative Long-Horizon Candidate Value from Reasoning Model Representations The paper constructs a dataset to show that relative candidate token value in multi-step reasoning is linearly decodable from model representations. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Analyzes text reasoning hidden states without multimodal capabilities.
AlphaPilot: Multi-Turn Tool-Using LLM Agents for Adaptive Alpha Mining with Reinforcement Learning The paper introduces a reinforcement learning framework that trains tool-using agents to generate and refine financial alpha factor formulas. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Deals with financial text and market data, not multimodal VLMs.
AlphaPilot: Multi-Turn Tool-Using LLM Agents for Adaptive Alpha Mining with Reinforcement Learning The paper introduces a reinforcement learning framework that trains tool-using agents to generate and refine financial alpha factor formulas. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on financial return optimization rather than safety guardrails.
AlphaPilot: Multi-Turn Tool-Using LLM Agents for Adaptive Alpha Mining with Reinforcement Learning The paper introduces a reinforcement learning framework that trains tool-using agents to generate and refine financial alpha factor formulas. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Focuses on agent tool queries rather than RAG architectures.
AlphaPilot: Multi-Turn Tool-Using LLM Agents for Adaptive Alpha Mining with Reinforcement Learning The paper introduces a reinforcement learning framework that trains tool-using agents to generate and refine financial alpha factor formulas. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not address model compression, pruning, or serving efficiency.
AlphaPilot: Multi-Turn Tool-Using LLM Agents for Adaptive Alpha Mining with Reinforcement Learning The paper introduces a reinforcement learning framework that trains tool-using agents to generate and refine financial alpha factor formulas. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes an alpha-mining agent rather than an LLM benchmark.
AlphaPilot: Multi-Turn Tool-Using LLM Agents for Adaptive Alpha Mining with Reinforcement Learning The paper introduces a reinforcement learning framework that trains tool-using agents to generate and refine financial alpha factor formulas. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Generates mathematical alpha formula expressions, not software engineering.
Concept Representations in Vector Space: Quantifying Sharedness and Idiosyncrasy of Meaning The paper develops a measurement framework using sentence embeddings to quantify shared versus individual-specific variation in concept definitions. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Analyzes sentence embeddings of definitions, not autonomous agents.
Concept Representations in Vector Space: Quantifying Sharedness and Idiosyncrasy of Meaning The paper develops a measurement framework using sentence embeddings to quantify shared versus individual-specific variation in concept definitions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on psycholinguistic measurement rather than LLM benchmarking.
Concept Representations in Vector Space: Quantifying Sharedness and Idiosyncrasy of Meaning The paper develops a measurement framework using sentence embeddings to quantify shared versus individual-specific variation in concept definitions. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Studies cognitive semantics, not code generation or repair.
Concept Representations in Vector Space: Quantifying Sharedness and Idiosyncrasy of Meaning The paper develops a measurement framework using sentence embeddings to quantify shared versus individual-specific variation in concept definitions. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Studies semantic concept variance, not retrieval-augmented generation systems.
Concept Representations in Vector Space: Quantifying Sharedness and Idiosyncrasy of Meaning The paper develops a measurement framework using sentence embeddings to quantify shared versus individual-specific variation in concept definitions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not cover model compression, distillation, or serving efficiency.
Concept Representations in Vector Space: Quantifying Sharedness and Idiosyncrasy of Meaning The paper develops a measurement framework using sentence embeddings to quantify shared versus individual-specific variation in concept definitions. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Investigates text embeddings without multimodal vision or audio inputs.
Concept Representations in Vector Space: Quantifying Sharedness and Idiosyncrasy of Meaning The paper develops a measurement framework using sentence embeddings to quantify shared versus individual-specific variation in concept definitions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Analyzes human concept representation rather than deployment safety risks.
A Step in the Right Place: Understanding and Optimizing Timestep Placement in Continuous Diffusion Language Models The paper introduces an algorithm to optimize denoising timestep placement in continuous diffusion language models to improve generation quality. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Focuses on continuous diffusion decoders, not agent planning.
A Step in the Right Place: Understanding and Optimizing Timestep Placement in Continuous Diffusion Language Models The paper introduces an algorithm to optimize denoising timestep placement in continuous diffusion language models to improve generation quality. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses diffusion sampling efficiency rather than safety or reliability.

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