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
RegExtract: A Benchmark for PFAS Regulatory Information Extraction with LLMs The paper introduces an information extraction benchmark for extracting structured regulatory fields and source provenance from dense environmental policy documents. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies prompting and extraction methods, not model efficiency.
Lumen-bench: Cross-Lingual Safety Gaps and the Say-Do Problem in Tool-Calling Large Language Models The paper introduces a multilingual benchmark evaluating safety and discrepancies between verbal refusals and tool actions in tool-calling language models. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Evaluates tool use and refusal, not software engineering.
Lumen-bench: Cross-Lingual Safety Gaps and the Say-Do Problem in Tool-Calling Large Language Models The paper introduces a multilingual benchmark evaluating safety and discrepancies between verbal refusals and tool actions in tool-calling language models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not address model compression, quantization, or distillation.
Lumen-bench: Cross-Lingual Safety Gaps and the Say-Do Problem in Tool-Calling Large Language Models The paper introduces a multilingual benchmark evaluating safety and discrepancies between verbal refusals and tool actions in tool-calling language models. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Text and tool-calling benchmark without multimodal components.
Lumen-bench: Cross-Lingual Safety Gaps and the Say-Do Problem in Tool-Calling Large Language Models The paper introduces a multilingual benchmark evaluating safety and discrepancies between verbal refusals and tool actions in tool-calling language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Focuses on tool-calling safety rather than retrieval-augmented grounding.
Compute-Optimal Scaling Laws for Spiking Language Models The paper presents an empirical scaling law study measuring parameter and compute trade-offs in spiking neural network language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Analyzes compute scaling laws rather than LLM evaluation benchmarks.
Compute-Optimal Scaling Laws for Spiking Language Models The paper presents an empirical scaling law study measuring parameter and compute trade-offs in spiking neural network language models. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Studies spiking neural network scaling, not LLM agents.
Compute-Optimal Scaling Laws for Spiking Language Models The paper presents an empirical scaling law study measuring parameter and compute trade-offs in spiking neural network language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Examines compute scaling tradeoffs rather than deployment safety guardrails.
Compute-Optimal Scaling Laws for Spiking Language Models The paper presents an empirical scaling law study measuring parameter and compute trade-offs in spiking neural network language models. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on text language modeling without multimodal components.
Compute-Optimal Scaling Laws for Spiking Language Models The paper presents an empirical scaling law study measuring parameter and compute trade-offs in spiking neural network language models. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Examines general language modeling scaling laws, not code generation.
Compute-Optimal Scaling Laws for Spiking Language Models The paper presents an empirical scaling law study measuring parameter and compute trade-offs in spiking neural network language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Does not investigate retrieval-augmented generation architectures.
Where Did We Go Wrong? Localizing Errors to Repair Multi-Turn Conversations The paper presents a framework to probabilistically localize user-intent misinterpretations and repair conversational states in multi-turn assistant systems. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not cover model quantization, pruning, or efficient inference.
Where Did We Go Wrong? Localizing Errors to Repair Multi-Turn Conversations The paper presents a framework to probabilistically localize user-intent misinterpretations and repair conversational states in multi-turn assistant systems. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Does not address external document retrieval or augmented generation.
Where Did We Go Wrong? Localizing Errors to Repair Multi-Turn Conversations The paper presents a framework to probabilistically localize user-intent misinterpretations and repair conversational states in multi-turn assistant systems. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Deals with conversational assistant state repair, not code generation.
Where Did We Go Wrong? Localizing Errors to Repair Multi-Turn Conversations The paper presents a framework to probabilistically localize user-intent misinterpretations and repair conversational states in multi-turn assistant systems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents an error-recovery framework rather than an evaluation benchmark.
Where Did We Go Wrong? Localizing Errors to Repair Multi-Turn Conversations The paper presents a framework to probabilistically localize user-intent misinterpretations and repair conversational states in multi-turn assistant systems. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Addresses text dialogue interactions without multimodal processing.
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	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Studies propositional text linking without multimodal inputs.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes a linking architecture rather than an evaluation benchmark.
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	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	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not study model pruning, quantization, or efficient serving.
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 safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on symbolic inference accuracy rather than deployment safety.
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	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Deals with natural language inference, not RAG architectures.
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	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	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on communicative interaction rather than multimodal vision-language models.
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.
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	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	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 safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses dataset active learning rather than AI deployment safety risks.
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	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.
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	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	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	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	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	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	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	AI agents via arxiv · 15 Oct, 06:18	Not relevant Focuses on KV cache state reuse rather than multi-step agents.
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	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.
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	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	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 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	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Analyzes linguistic trait embeddings, not 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	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	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.
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.
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.

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