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
SciTBERT: A family of chronologically consistent language models for scientific and technological language processing Releases BERT models pre-trained with yearly chronological cutoffs to reduce lookahead bias in scientific and patent text mining. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on chronological pretraining rather than model compression or inference efficiency.
SciTBERT: A family of chronologically consistent language models for scientific and technological language processing Releases BERT models pre-trained with yearly chronological cutoffs to reduce lookahead bias in scientific and patent text mining. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Applies NLP to scientific papers and patents rather than code or software engineering.
SciTBERT: A family of chronologically consistent language models for scientific and technological language processing Releases BERT models pre-trained with yearly chronological cutoffs to reduce lookahead bias in scientific and patent text mining. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on avoiding chronological lookahead bias rather than deployment guardrails or safety.
DiffuPlex: Accelerating Full-Duplex Spoken Dialog Models via Rolling Masked Diffusion Presents a rolling masked diffusion method that accelerates full-duplex spoken dialog models by forecasting multiple future interaction frames. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on spoken dialog diffusion models rather than code generation.
DiffuPlex: Accelerating Full-Duplex Spoken Dialog Models via Rolling Masked Diffusion Presents a rolling masked diffusion method that accelerates full-duplex spoken dialog models by forecasting multiple future interaction frames. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents an inference acceleration framework rather than an evaluation benchmark.
DiffuPlex: Accelerating Full-Duplex Spoken Dialog Models via Rolling Masked Diffusion Presents a rolling masked diffusion method that accelerates full-duplex spoken dialog models by forecasting multiple future interaction frames. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Targets inference speedup rather than deployment safety, jailbreaks, or guardrails.
DiffuPlex: Accelerating Full-Duplex Spoken Dialog Models via Rolling Masked Diffusion Presents a rolling masked diffusion method that accelerates full-duplex spoken dialog models by forecasting multiple future interaction frames. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Deals with spoken dialog inference latency rather than retrieval-augmented generation.
DiffuPlex: Accelerating Full-Duplex Spoken Dialog Models via Rolling Masked Diffusion Presents a rolling masked diffusion method that accelerates full-duplex spoken dialog models by forecasting multiple future interaction frames. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on acoustic speech generation inference rather than multi-step agent planning.
Language Models as AI Research World Models Evaluates whether language models can act as world models to predict empirical experimental outcomes across AI research workflows. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on predicting training experiment results without multimodal integration.
Language Models as AI Research World Models Evaluates whether language models can act as world models to predict empirical experimental outcomes across AI research workflows. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Studies research optimization rather than deployment safety, jailbreaks, or robustness.
Language Models as AI Research World Models Evaluates whether language models can act as world models to predict empirical experimental outcomes across AI research workflows. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on research workflow outcome prediction rather than software engineering.
Language Models as AI Research World Models Evaluates whether language models can act as world models to predict empirical experimental outcomes across AI research workflows. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Studies experiment outcome modeling rather than document retrieval-augmented generation.
Language Models as AI Research World Models Evaluates whether language models can act as world models to predict empirical experimental outcomes across AI research workflows. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Studies predictive models for experiment selection rather than model compression techniques.
Language Models as AI Research World Models Evaluates whether language models can act as world models to predict empirical experimental outcomes across AI research workflows. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Focuses on world models for experiment prediction rather than LLM benchmarking methods.
TokenRouter: Efficient Serving System for Token-Level LLM Routing Designs an inference serving system optimized for dynamic, token-level routing across multiple language models. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Deals with LLM inference systems rather than code synthesis or program repair.
TokenRouter: Efficient Serving System for Token-Level LLM Routing Designs an inference serving system optimized for dynamic, token-level routing across multiple language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Deals with serving infrastructure rather than retrieval-augmented generation.
TokenRouter: Efficient Serving System for Token-Level LLM Routing Designs an inference serving system optimized for dynamic, token-level routing across multiple language models. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on systems-level token routing across LLMs rather than agentic planning.
TokenRouter: Efficient Serving System for Token-Level LLM Routing Designs an inference serving system optimized for dynamic, token-level routing across multiple language models. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes an inference system architecture rather than a benchmarking framework.
TokenRouter: Efficient Serving System for Token-Level LLM Routing Designs an inference serving system optimized for dynamic, token-level routing across multiple language models. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on LLM serving infrastructure without multimodal components.
TokenRouter: Efficient Serving System for Token-Level LLM Routing Designs an inference serving system optimized for dynamic, token-level routing across multiple language models. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses system throughput and serving efficiency rather than AI safety or guardrails.
NativeScope: Relation-Localized Retrieval over Native Topology with a Correct Anchor Proposes a scope-then-rank retrieval method using document topology and relational structure rather than pure semantic similarity. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Evaluates a specific retrieval algorithm rather than establishing a broad LLM benchmark.
NativeScope: Relation-Localized Retrieval over Native Topology with a Correct Anchor Proposes a scope-then-rank retrieval method using document topology and relational structure rather than pure semantic similarity. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on document retrieval ranking rather than autonomous agent systems.
NativeScope: Relation-Localized Retrieval over Native Topology with a Correct Anchor Proposes a scope-then-rank retrieval method using document topology and relational structure rather than pure semantic similarity. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Addresses document retrieval rather than code generation or software engineering.
NativeScope: Relation-Localized Retrieval over Native Topology with a Correct Anchor Proposes a scope-then-rank retrieval method using document topology and relational structure rather than pure semantic similarity. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Applies to text retrieval over document structure without multimodal data.
NativeScope: Relation-Localized Retrieval over Native Topology with a Correct Anchor Proposes a scope-then-rank retrieval method using document topology and relational structure rather than pure semantic similarity. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on retrieval precision rather than AI safety or reliability.
NativeScope: Relation-Localized Retrieval over Native Topology with a Correct Anchor Proposes a scope-then-rank retrieval method using document topology and relational structure rather than pure semantic similarity. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on retrieval algorithms rather than model compression or efficient inference.
EgoVoice: Proactive Spoken Assistance from Egocentric Multimodal Streams Develops a framework for egocentric multimodal assistants to decide both when to speak and what guidance to provide. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes a model framework rather than general LLM evaluation benchmarks.
EgoVoice: Proactive Spoken Assistance from Egocentric Multimodal Streams Develops a framework for egocentric multimodal assistants to decide both when to speak and what guidance to provide. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Designed for wearable consumer AR assistance rather than industrial manufacturing or inspection.
EgoVoice: Proactive Spoken Assistance from Egocentric Multimodal Streams Develops a framework for egocentric multimodal assistants to decide both when to speak and what guidance to provide. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on multimodal interactive assistance rather than model compression or efficient serving.
EgoVoice: Proactive Spoken Assistance from Egocentric Multimodal Streams Develops a framework for egocentric multimodal assistants to decide both when to speak and what guidance to provide. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Does not address programming, code generation, or software engineering.
EgoVoice: Proactive Spoken Assistance from Egocentric Multimodal Streams Develops a framework for egocentric multimodal assistants to decide both when to speak and what guidance to provide. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on egocentric streaming multimodal assistance rather than retrieval-augmented generation.
EgoVoice: Proactive Spoken Assistance from Egocentric Multimodal Streams Develops a framework for egocentric multimodal assistants to decide both when to speak and what guidance to provide. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses proactive assistance timing rather than deployment safety, jailbreaks, or guardrails.
HarnessSQL: Harness-Native Training for SQL Agents in Realistic Database Environments Introduces a post-training framework that trains text-to-SQL agents directly inside interactive database execution environments. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Deals exclusively with text and SQL code without multimodal processing.
HarnessSQL: Harness-Native Training for SQL Agents in Realistic Database Environments Introduces a post-training framework that trains text-to-SQL agents directly inside interactive database execution environments. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Deals with interactive database querying rather than document retrieval-augmented generation.
HarnessSQL: Harness-Native Training for SQL Agents in Realistic Database Environments Introduces a post-training framework that trains text-to-SQL agents directly inside interactive database execution environments. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on execution accuracy in SQL tasks rather than safety, guardrails, or alignment.
HarnessSQL: Harness-Native Training for SQL Agents in Realistic Database Environments Introduces a post-training framework that trains text-to-SQL agents directly inside interactive database execution environments. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents a training framework rather than proposing a general LLM benchmark.
HarnessSQL: Harness-Native Training for SQL Agents in Realistic Database Environments Introduces a post-training framework that trains text-to-SQL agents directly inside interactive database execution environments. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Addresses agent training for SQL tasks rather than model compression or serving efficiency.
SparseDecoding: Decoding-Aware Pruning for Accurate and Efficient LLM Inference Identifies distribution shift during autoregressive decoding and proposes a decoding-aware pruning method to accelerate model inference. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes an inference pruning technique rather than an evaluation benchmark.
SparseDecoding: Decoding-Aware Pruning for Accurate and Efficient LLM Inference Identifies distribution shift during autoregressive decoding and proposes a decoding-aware pruning method to accelerate model inference. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Optimizes language model inference without multimodal inputs.
SparseDecoding: Decoding-Aware Pruning for Accurate and Efficient LLM Inference Identifies distribution shift during autoregressive decoding and proposes a decoding-aware pruning method to accelerate model inference. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on model pruning for decoding speedup rather than agent workflows.
SparseDecoding: Decoding-Aware Pruning for Accurate and Efficient LLM Inference Identifies distribution shift during autoregressive decoding and proposes a decoding-aware pruning method to accelerate model inference. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Applies to LLM decoding efficiency broadly, not software engineering or coding tasks.
SparseDecoding: Decoding-Aware Pruning for Accurate and Efficient LLM Inference Identifies distribution shift during autoregressive decoding and proposes a decoding-aware pruning method to accelerate model inference. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on inference latency rather than AI safety, jailbreaks, or alignment.
SparseDecoding: Decoding-Aware Pruning for Accurate and Efficient LLM Inference Identifies distribution shift during autoregressive decoding and proposes a decoding-aware pruning method to accelerate model inference. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Addresses weight pruning during decoding rather than retrieval-augmented generation.
VFold: Symmetry-Aware Cross-Layer Value Cache Compression Presents a symmetry-aware value cache merging method to compress key-value memory during long-context decoding without architectural changes. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents a systems optimization technique rather than an evaluation benchmark.
VFold: Symmetry-Aware Cross-Layer Value Cache Compression Presents a symmetry-aware value cache merging method to compress key-value memory during long-context decoding without architectural changes. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on transformer memory architecture without multimodal data.
VFold: Symmetry-Aware Cross-Layer Value Cache Compression Presents a symmetry-aware value cache merging method to compress key-value memory during long-context decoding without architectural changes. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on general LLM memory efficiency rather than code generation or program repair.
VFold: Symmetry-Aware Cross-Layer Value Cache Compression Presents a symmetry-aware value cache merging method to compress key-value memory during long-context decoding without architectural changes. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Optimizes transformer KV cache compression rather than retrieval-augmented generation.
VFold: Symmetry-Aware Cross-Layer Value Cache Compression Presents a symmetry-aware value cache merging method to compress key-value memory during long-context decoding without architectural changes. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on KV cache memory optimization rather than LLM agent architectures.
VFold: Symmetry-Aware Cross-Layer Value Cache Compression Presents a symmetry-aware value cache merging method to compress key-value memory during long-context decoding without architectural changes. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses hardware efficiency and memory usage rather than AI safety or reliability.
Which Skill to Distill? SGUID: Selecting a Compact Skill Bank for Model-Skill Co-Evolution Proposes a method to select a compact subset of procedural skills that provide effective training signals during model distillation. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on language model distillation without multimodal modalities.
Which Skill to Distill? SGUID: Selecting a Compact Skill Bank for Model-Skill Co-Evolution Proposes a method to select a compact subset of procedural skills that provide effective training signals during model distillation. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on distillation performance rather than AI safety, jailbreaks, or deployment guardrails.
Which Skill to Distill? SGUID: Selecting a Compact Skill Bank for Model-Skill Co-Evolution Proposes a method to select a compact subset of procedural skills that provide effective training signals during model distillation. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces a distillation method rather than a general LLM evaluation benchmark.
Which Skill to Distill? SGUID: Selecting a Compact Skill Bank for Model-Skill Co-Evolution Proposes a method to select a compact subset of procedural skills that provide effective training signals during model distillation. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Investigates general skill distillation rather than software development or coding models.
Which Skill to Distill? SGUID: Selecting a Compact Skill Bank for Model-Skill Co-Evolution Proposes a method to select a compact subset of procedural skills that provide effective training signals during model distillation. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on knowledge distillation of procedural guidance rather than autonomous agent systems.
Which Skill to Distill? SGUID: Selecting a Compact Skill Bank for Model-Skill Co-Evolution Proposes a method to select a compact subset of procedural skills that provide effective training signals during model distillation. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Studies selecting skills for model distillation rather than run-time retrieval-augmented generation.
Latent Core Tokenizer: Compress, but Meaningfully Introduces a multilingual tokenizer that uses structural discovery and linguistic constraints to improve downstream model representations. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses strictly on multilingual text tokenization without multimodal components.
Latent Core Tokenizer: Compress, but Meaningfully Introduces a multilingual tokenizer that uses structural discovery and linguistic constraints to improve downstream model representations. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces a tokenization method rather than an LLM evaluation methodology or benchmark.
Latent Core Tokenizer: Compress, but Meaningfully Introduces a multilingual tokenizer that uses structural discovery and linguistic constraints to improve downstream model representations. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Studies multilingual vocabulary tokenization without addressing software engineering or code LLMs.
Latent Core Tokenizer: Compress, but Meaningfully Introduces a multilingual tokenizer that uses structural discovery and linguistic constraints to improve downstream model representations. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Studies linguistic compression rather than AI safety, guardrails, or deployment reliability.
Latent Core Tokenizer: Compress, but Meaningfully Introduces a multilingual tokenizer that uses structural discovery and linguistic constraints to improve downstream model representations. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on subword tokenization algorithms rather than retrieval or grounding mechanisms.

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