Method to extract tokenization oracles from chat APIs described
A Reddit post outlines a technique to reconstruct a closed-source LLM tokenizer using two oracles derived from standard chat APIs: a token length oracle and a prefix token oracle. The prefix oracle resolves merge order ambiguities by leveraging the model's text repetition capability. This method could enable researchers to analyze tokenization without direct access to the tokenizer.
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ContextOps: open-source tool to audit and optimize LLM prompt context
ContextOps is an open-source tool that analyzes LLM prompts to detect token waste such as duplicated retrieval chunks, bloated system prompts, oversized conversation history, and repeated tool outputs. It helps developers reduce costs and improve model consistency by auditing what goes into the prompt before inference.
Developer seeks feedback on fine-tuning LoRA for conversation state extraction in long LLM chats
A developer is working on a side project to improve AI conversation continuity by training a small model to extract structured conversation state from chat chunks, rather than relying on summarization. They are seeking feedback on their approach involving fine-tuning a LoRA, dataset design, and long-context systems.
New model with 500K-token context and $2/$6 pricing shifts cost calculus
A model offering a 500,000-token context window at $2 per million input tokens and $6 per million output tokens has been released, drawing attention for its cost-effectiveness. The pricing and context length are seen as significant for applications requiring long-context processing, potentially changing the competitive landscape before benchmark comparisons are even made.
Anthropic explains LLM's challenge in distinguishing own thoughts from user input
Anthropic published a technical explanation of how LLMs like Claude perceive conversation as a single continuous text stream, making it difficult to distinguish between their own generated text and user input. The post uses a snapshot of Claude's response to illustrate the problem, highlighting the fundamental difference between the structured chat interface users see and the raw token sequence the model processes.
Researchers identify modality interference as root cause of full-duplex SLM degradation
A new paper on arXiv (July 7, 2026) presents a fine-grained analysis of optimization dynamics in full-duplex Spoken Language Models (SLMs), identifying severe modality interference as the root cause of knowledge degradation and compromised semantic integrity. The work aims to enable more natural and intelligent full-duplex SLMs.