arXiv papers
Hand-picked recent LLM frontier papers, with practitioner notes on what to use and skip.
WEAVER: a world model for robotic manipulation that simultaneously achieves fidelity, long-horizon consistency, and fast inference
WEAVER (CMU) is a learned world model for robotic manipulation that resolves the fidelity-consistency-speed trilemma in one architecture. High accuracy, long-horizon coherence, and real-time inference together enable test-time planning with minimal real-world interaction.
ART: Fine-tuning frozen multimodal LLMs by optimizing visual soft-tokens with reinforcement learning — no weight updates required
ART fine-tunes frozen multimodal LLMs by optimizing only visual input tokens, enabling RL-based adaptation on pre-compiled inference graphs like vLLM. Visual soft-token reinforcement on a frozen backbone achieves effective task adaptation with no weight updates.
Latent Context Language Models: encoder-decoder compression trained on 350B tokens beats KV-cache pruning — 81% on GSM8K at 16x compression where baselines drop to 0%
A 15-author team spanning the Goldstein, Goldblum and Izmailov groups revives encoder-decoder context compression at scale: a 0.6B encoder plus 4B decoder trained on roughly 350B tokens sets a new speed/memory/accuracy Pareto frontier, with models and code released.
"When Retrieval Doesn't Help": a 5-model, 10-dataset biomedical RAG study finds gains of just 1-2 points — and the backbone matters more than the retriever
A new arXiv study sweeps 5 open-weight models, 10 biomedical QA datasets, 4 retrievers and 4 corpora, and finds RAG adds only 1-2 points over a no-retrieval baseline. The backbone model matters more than the retriever — a sobering result for anyone bolting RAG onto an LLM.
LazyAttention: position-agnostic KV reuse that unsticks the RAG cache bottleneck
A new ICML 2026 paper, LazyAttention (arXiv:2606.04302), tackles a stubborn limitation of KV caching for retrieval-augmented generation: because positional information is baked into the cache, a chunk cached at one position cannot be reused at another. The authors defer
Your "Reasoning Probe" May Just Be Reading the Format: A Cautionary arXiv Result
A new arXiv paper (2606.02907) shows that linear probes hitting 100% accuracy at separating deductive, inductive, and abductive reasoning in Qwen3-14B hidden states collapse to chance once you control for task-format confounds like source dataset, option count, and response
Roll Out and Roll Back: making diffusion LLMs revoke their own mistakes for 6x faster decoding
A new arXiv paper introduces WINO, a training-free decoding trick for diffusion language models that drafts many tokens at once, then verifies and re-masks the unreliable ones. It reports up to 6.1x fewer denoising steps on GSM8K while accuracy actually rises from 73.24% to
LatentRAG moves agentic RAG reasoning into latent space, cutting inference latency ~90%
A new arXiv paper, LatentRAG, shifts the multi-step reasoning and query generation of agentic RAG from token-by-token text into continuous latent space, matching explicit-agent accuracy while cutting inference latency by roughly 90%.
AutoTTS — LLM agents discover their own test-time scaling strategies for $39.90
AutoTTS reframes test-time scaling as a controller-synthesis problem: an LLM agent discovers when to branch, continue, probe, prune, or stop — instead of hand-tuned best-of-N. The full discovery loop costs $39.90 / 160 min and beats manual baselines on the accuracy-cost tradeoff.
Agentic Context Decay: Why Multi-Tool Agents Lose Coherence Past 7 Calls
Empirical bound on multi-step agent coherence: success rate drops sharply at the 7th tool call across Claude 4.7, GPT-5, Gemini 3 Pro. Mitigations via tool-trace summarization recover most of the loss.
The Prompt Cache Economy: 73% of LLM Cost Hides in Cacheable Prefixes
Trace analysis of 14M production LLM requests: 73% of input tokens repeat across requests within a 5-minute window. Quantifies cost savings from prompt caching across providers — Anthropic 5-min TTL captures 81% of savings.
Quantifying the MCP Supply Chain: 200K Public Servers, 14% Vulnerable to Tool Hijack
Empirical scan of 200,073 public MCP servers across npm/PyPI registries: 14.3% expose tool descriptions vulnerable to prompt-injection hijack; 4.8% leak credentials in unfiltered logs. First quantitative MCP threat model.
Tuna-2: Pixel Embeddings Beat Vision Encoders for Multimodal
Native unified multimodal model that encodes images with simple patch embeddings — no VAE, no separate vision encoder. Wins at scale on fine-grained perception.
Outcome Rewards Do Not Guarantee Verifiable or Causally Important Reasoning
Introduces CIR (Causal Importance of Reasoning) and SR (Sufficiency of Reasoning) metrics. RLVR improves accuracy but does not reliably improve CIR or SR.
TEMPO: Scaling Test-time Training for Large Reasoning Models
Test-time training framework that interleaves policy refinement on unlabeled questions with periodic critic recalibration. OLMO3-7B 33.0% → 51.1% on AIME 2024.
NPUMoE: Efficient MoE LLM Inference with Apple Silicon NPUs
Runtime that handles MoE on Apple NPUs via offline calibration, static capacity tiers, and load-aware graph residency. 1.32-5.55× lower latency on M-series chips.
Scaling Test-Time Compute for Agentic Coding
Argues test-time scaling for long-horizon coding agents is a representation problem. Reports Claude Opus 4.5 going from 70.9% to 77.6% on SWE-Bench Verified.
AgentForge: Execution-Grounded Multi-Agent LLM Framework for Autonomous SWE
Proposes execution-grounded verification as a first-class principle: every code change must survive Docker-sandboxed execution. Reaches 40.0% on SWE-Bench Lite.
LASA: Language-Agnostic Semantic Alignment at the Semantic Bottleneck
Identifies a "semantic bottleneck" layer where representations are language-neutral. Anchoring alignment there drops attack success on LLaMA-3.1-8B from 24.7% to 2.8%.
The Amazing Agent Race: Strong Tool Users, Weak Navigators
Builds a DAG-puzzle Wikipedia benchmark. Across 1,400 instances and three frameworks, the best system hits 37.2% — navigation errors dominate, not tool-use errors.