RLHF & Preference Learning - Weekly Roundup

Timbre drift in multi-turn expressive voice agents can be eliminated by replacing implicit context embeddings with explicitly supervised style instructions optimized via rejection sampling and context-aware DPO.

A diagnostic framework that measures point accuracy alongside dispersion retention, the ratio of predicted to human standard deviation, on 10,000 respondent--question pairs from the World Values Survey (WVS) spanning twelve countries and six continents is developed.

An interval-censored pre-recognition transition, an independent as-of reference standard, and a prespecified recognition proxy are defined to make the claim testable.

A progressive four-stage framework that addresses three clinically critical gaps in TCM prescription generation and substantially improves prescription quality over zero-shot baselines is proposed, with a 7B model (Mistral-7B) surpassing zero-shot GPT-5 on all three TCM evaluation metrics.

Under a local anti-concentration condition on the exit-time law, it is shown that strong exit-time approximation transfers to strong approximation of the failure indicator, enabling existing and future techniques whose applicability depends on improved strong approximation.

Qwen-Audio-Agent is presented, a harness that combines full-duplex voice interaction with asynchronous task execution through a foreground-background architecture that supports the complementary use of direct tool calls for immediate operations and backend delegation for multi-step tasks.

ALIBI is presented, a semantic cover story attack against frontier LLM-based malware analyzers that adds a small, non-executed read-only section to a compiled binary, containing a coherent but false security product narrative, without altering imports or executable behavior.

CoFree (Collapse-Free Reasoning Embedding), a two-stage framework that progressively integrates LLM reasoning into query and document embedding optimization while preserving reasoning quality, is proposed.

The probabilistic Allen algebra (PAA) is developed, a generative and complete extension in which relation probabilities are derived from distributions over interval boundaries rather than assigned as scores.

Results show that clean CTF performance understates vulnerability to deceptive task evidence, and AgentLSD isolates this effect and provides a reproducible benchmark for studying it.

A three-tier observability framework comprising catalog metadata, passive compliance signals, and live vulnerability analysis is applied to empirically characterize the public MCP server ecosystem, identifying a Security-Observability Tradeoff observed in the current ecosystem.