Speech transcripts used as long-term memory must preserve both words and stable speaker identities. Existing meeting-transcription metrics either ignore speakers or remap anonymous speakers independently in each recording, so they cannot measure whether the same person retains one identity across meetings. We evaluate persistent speaker attribution with Speaker Identified cpWER (SI-cpWER), which scores a corpus under one global speaker-ID assignment. The benchmark covers five commercial diarize-then-identify cascades, two open academic baselines, and ThyVoice on the full 129-meeting CHiME-8 NOTSOFAR evaluation set in clean and noiseaugmented form, plus CHiME-6. ThyVoice is our end-to-end reference system; it repairs overlap and gates the evidence used to create and update voiceprints. Requiring persistent identity changes the commercial ranking: ThyVoice records lower SI-cpWER than every evaluated commercial cascade in all three conditions and the lowest mean in the full panel, 47.13 versus 54.75 for the next system. Complementary lexical, diarization, per-recording attribution, and speaker-clustering diagnostics characterize upstream error surfaces in the final attributed record. These results show why persistent attribution must be evaluated directly in systems that reuse conversations across time.
Translation pending · showing the source descriptionThe chart will appear after repeat observations. The current metric comes from the source.
0 source votesReal observations only. History before source connection is not reconstructed.
The paper investigates the scaling properties of on-policy distillation (OPD) in reinforcement learning, focusing on how capabilities transfer between different model scales. It identifies a useful-transfer regime where held-out accuracy increases linearly with the reverse KL divergence from the student's initialization, and finds that smaller teachers can outperform larger ones in capability transfer.
5 days agoDaily PapersThe paper introduces VisionHOPE, a novel visual backbone that functions as a self-modifying learning system, allowing the model to co-evolve what it remembers and how it learns within an image. It uses five coupled memories and a stability-matched step-size control scheme to ensure stable learning dynamics, achieving competitive results on benchmark datasets like ImageNet-1K, COCO, and ADE20K.
4 days agoDaily PapersThe paper explores phase sensitivity in models using chunked KV-cache compression, where retrieval performance varies systematically across different phases of compressed token windows. It shows that long-context retrieval accuracy can differ by up to 40 percentage points between phases, highlighting the need for phase-specific evaluation.
3 days agoDaily PapersThe paper explores test-time AI-for-AI, focusing on how a Builder can create better execution environments for a Target while keeping both models' weights fixed. It introduces Meta-Skill, principles derived from Target's execution feedback, which improve performance in tasks like Harness-Bench and NewtonBench.
2 days agoDaily PapersThe paper addresses co-cheating in self-evolving search agents, where proposers and solvers develop shared errors leading to misleading internal rewards. It introduces Multi-Sample Verification (MSV) and CrossFit methods to mitigate this issue, showing improvements in reducing false agreement and enhancing search performance.
yesterdayLessWrongFrontier models show different decision theory preferences based on the perceived user background, favoring FDT/UDT when not influenced by academic philosophy cues and CDT when prompted to adopt an academic perspective. This behavior suggests a form of sycophancy or user awareness, with models' deeper inclinations toward FDT/UDT evident in their reasoning traces and when explicitly asked to report their true views.
yesterday