513+ Citations · h-index 12 
Published:
A speech model answers a spoken question in one word. We read its middle layers while it listened - the answer was already there, in words it never says. Read more
Published:
RL post-training runs on one fixed coefficient that must both protect the model and get out of its way. Subtract the advantage - one line. Read more
Published:
Voice agents could be measured, never improved. We closed the loop - two open models talking in native audio - and task success more than doubled. Read more
Published:
Voice agents could be measured, never improved. We closed the loop - two open models talking in native audio - and task success more than doubled. Read more
Published:
A speech model answers a spoken question in one word. We read its middle layers while it listened - the answer was already there, in words it never says. Read more
Published:
Telling an Audio LLM to think cost it 3.40 points. Rewarding the reasoning process instead earns 3.40 - the same margin, mirrored. Read more
Published:
RL post-training runs on one fixed coefficient that must both protect the model and get out of its way. Subtract the advantage - one line. Read more
Published:
RL post-training runs on one fixed coefficient that must both protect the model and get out of its way. Subtract the advantage - one line. Read more
Published:
A speech model answers a spoken question in one word. We read its middle layers while it listened - the answer was already there, in words it never says. Read more
Published:
A speech model answers a spoken question in one word. We read its middle layers while it listened - the answer was already there, in words it never says. Read more
Published:
Telling an Audio LLM to think cost it 3.40 points. Rewarding the reasoning process instead earns 3.40 - the same margin, mirrored. Read more
Published:
Voice agents could be measured, never improved. We closed the loop - two open models talking in native audio - and task success more than doubled. Read more
Published:
Telling an Audio LLM to think cost it 3.40 points. Rewarding the reasoning process instead earns 3.40 - the same margin, mirrored. Read more
Published:
RL post-training runs on one fixed coefficient that must both protect the model and get out of its way. Subtract the advantage - one line. Read more
Published:
Telling an Audio LLM to think cost it 3.40 points. Rewarding the reasoning process instead earns 3.40 - the same margin, mirrored. Read more
Published:
Voice agents could be measured, never improved. We closed the loop - two open models talking in native audio - and task success more than doubled. Read more