Jalapeño is a custom inference chip from OpenAI that delivers faster, more power-efficient AI inference, with higher throughput and lower latency for modern models.

arXiv:2608.21362v1 Announce Type: new
Abstract: Transformer-based large language models (LLMs) incur high prefill latency because key-value (KV) tensors must be recomputed for each request. Existing prefix-caching systems reduce this cost but require prompts to share a leading contiguous prefix, limiting effectiveness when shared content appears at arbitrary positions. We present KVBoost, a chunk-level KV cache reuse system for HuggingFace-compatible decoder models that enables reuse regardless of content position. KVBoost introduces a dual-hash keying scheme that separates positional identity (prefix hash) from content identity (content hash), supporting both exact and approximate cache matches. To address attention boundary errors from independently cached chunks, KVBoost employs two repair strategies: SelectiveRecompute, which re-encodes boundary regions, and CacheBlendRecompute, which identifies and recomputes high-deviation tokens after a probe pass. The system further incorporates asymmetric KV quantization (int8/int4), adaptive chunk boundary splitting, and importance-weighted eviction under a fixed memory budget. Evaluated on Qwen/Qwen2.5-3B over 1,000 bug-localization samples, KVBoost achieves a 4.49x reduction in time-to-first-token (142.4 ms vs. 639.1 ms) and outperforms prefix caching by 16%, with no loss in accuracy (99.2% vs. 99.1%). KVBoost provides a practical, memory-bounded inference acceleration layer compatible with RoPE-based models without architectural modification.

arXiv:2608.21363v1 Announce Type: new
Abstract: A protocol is presented for recording the governance decisions of automated AI runtimes. When a runtime releases, blocks, defers, redacts, or escalates an individual output, AIREP records that decision as a single signed object that any party can check offline, independent of the runtime that produced it. A record carries the decision as one of a closed set of verbs under a stated policy basis, references its input, output, and evidence by hash rather than by value, and declares both what its evidence covers and what it does not. Records form a SHA-256 hash chain that binds each record to its position, so that tampering and gaps are detectable by recomputation. Vendor-, model-, and domain-specific content is confined to a single optional namespace, and a mechanical neutrality test keeps the shared format free of it. A reference implementation and a two-language conformance kit are described. Some implementation issues are considered, and problems such as alignment of the canonical form across implementations, freshness witnesses, and multi-runtime chains are exposed. The format is offered for adoption by any AI runtime that records governance decisions.

arXiv:2608.21366v1 Announce Type: new
Abstract: Driven by massive amounts of web-scale data, generative AI (GenAI) has achieved remarkable progress, enabling various applications in diverse sectors. The advances of GenAI have actuated practitioners to use AI-synthesized data for training next-generation AI models. Undeniably, using synthetic data has alleviated the increasing stringent demand for data supply. Unfortunately, it also introduces a new critical issue: in a self-consuming cycle between model and data, the model ultimately collapse, raising more trustworthiness concerns to GenAI. In recent years, increasingly more studies have investigated the phenomenon of model collapse (MC) and explored potential solutions to mitigate it. However, the review of the phenomenon of MC still remains blank. To fill this gap, this paper provides an up-to-date overview of these studies for consolidating and reviewing the progress of MC in different application scenarios and countermeasures for mitigating MC. We also highlight challenges and future research opportunities.

arXiv:2608.21372v1 Announce Type: new
Abstract: This report summarizes the work conducted on the Game of Hidden Rules (GOHR), focusing on reinforcement learning agents trained to infer hidden rules from trial-and-error feedback, representation design, rule difficulty analysis, transfer learning, generalization, and pseudo-bot-assisted human learning analysis. The report focuses on the Transformer-based A2C framework, Feature-Centric and Object-Centric representations, experimental findings, and classification of human learning data.

arXiv:2608.21374v1 Announce Type: new
Abstract: Literature reviews are essential to scientific progress, but rigorously evaluating automatically generated reviews remains difficult because many aspects of research utility depend on expert judgment rather than reference-overlap metrics. We introduce LitReview Arena, a battle-style evaluation platform with a structured protocol tailored to literature review quality: domain experts with AI paper-writing experience compare anonymized drafts, are matched to topics within their expertise, and provide dimension-wise outcomes over five literature-review-specific criteria. From this protocol, we collect approximately 3k expert judgments, each containing five dimension-wise outcomes, and show that even the strongest current systems win only 23.0% of decisive matches against human drafts on overall utility, while agentic LLMs such as Sonar Deep Research substantially outperform base language models by over 60%. We further find that existing LLM-as-a-judge methods are substantially misaligned with human experts (Spearman’s rho=0.467), especially on synthesis-heavy criteria such as paper structure and research suggestions. Using the collected preference data, we provide an expert-calibrated evaluator, LitJudge, which improves alignment to Spearman’s rho=0.78, comparable to inter-expert consistency; code and data are publicly available at https://github.com/VanellopeAsher/LitReview-Arena.