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When Should Agents Think? Adaptive Reasoning via Cross-Turn Estimation
The preprint proposes Reasoning Adaptation through Cross-Turn Estimation (RACE), which estimates when prior reasoning remains sufficient for subsequent actions. Its LoGiC procedure identifies reasoning turns whose…
Learning to Stop without Learning to Stop: Self-Supervised Confidence Training Improves Reasoning Efficiency
The preprint studies whether reasoning efficiency can improve without directly optimizing length or adding an inference-time stopping mechanism. It fine-tunes models to predict confidence at intermediate points in…
Learning the Cost of Reliable Inference
The paper proposes a procurement platform where model providers compete on task-level token prices through a reverse second-price auction. The platform learns provider quality and routes requests to a cost-competitive…
CliffCompaction: Cost-Efficient Compaction for Long-Horizon Coding Agents
CliffCompaction is an autocompaction technique for long-horizon coding agents that reduces context cost while preserving or improving benchmark performance. It only truncates or drops content rather than rephrasing it…
CompKV: Compensation-Aware KV Selection for Long-Context LLM Inference
CompKV selects token blocks jointly with the downstream compensation mechanism for sparse attention rather than choosing tokens only by attention mass. The preprint derives a block-level residual approximation based on…
Pinocchio: Fast Uncertainty Estimates for Black-Box Language Models
Pinocchio is an external calibrator for estimating response correctness from black-box language-model APIs without logits, weights, internal states, or fine-tuning access. The preprint reports a single-forward-pass…
DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression
The record describes a 552B-backbone multimodal mixture-of-experts model with up to one million tokens of context, activating 16B parameters during decode and 8B during prefill. It reports cross-layer KV reuse, FP4 KV…
Video DeltaNet: A Video-Native Hybrid Attention for Livestream Video Generation
Video DeltaNet combines local Softmax attention with bidirectional linear memory for long-range video context. Its Video Delta Attention updates memory once per frame, with separate projections and gates for…
Pre-Compiled Pipeline Shards for Distributed LLM Inference on Intel AI PC Fleets
The authors propose a method to utilize multiple Intel AI PCs with integrated GPUs and NPUs, even when they have insufficient memory for large models like a 70B-parameter LLM. The method involves breaking the model…
Solution of the Hempel's statistical ambiguity problem and Causal AI
The paper presents a solution to Hempel's statistical ambiguity problem in inductive-statistical inference by introducing the concept of Maximally Specific Causal Relationships (MSCRs). The MSCRs are derived from a…
Boost Inference Performance up to 15x on NVIDIA Blackwell Using DFlash Speculative Decoding
NVIDIA announced DFlash Speculative Decoding, an inference optimization technique for Blackwell GPUs targeting multi-agent AI workloads. The claimed performance improvement is up to 15x on NVIDIA Blackwell hardware.
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