OCL is a governance layer for LLM agents that cuts unsafe executions from 88% to near-zero and raises valid success from 12% to 96% in adversarial buyer-seller negotiations across frontier LLMs.
Omnichat: Enhancing spoken dialogue systems with scalable synthetic data for diverse scenarios
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EchoDistill applies noisy-to-clean self-distillation with GRPO to boost Audio LLM robustness, reporting 4.18% average GSR gains under strong noise.
ROGLE introduces automated pseudo region-sentence pairs via RSM and multi-granular learning to boost fine-grained alignment in text-based person search, plus the P-VLG benchmark with over 100k annotated regions.
DiscussLLM introduces a two-stage synthetic data pipeline to annotate multi-turn discussions with five intervention types and trains LLMs to time contributions via a silent token or proactive responses.
citing papers explorer
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Organizational Control Layer: Governance Infrastructure at the Execution Boundary of LLM Agent Systems
OCL is a governance layer for LLM agents that cuts unsafe executions from 88% to near-zero and raises valid success from 12% to 96% in adversarial buyer-seller negotiations across frontier LLMs.
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EchoDistill:Alignment Noisy-to-Clean Self-Distillation for Robust Audio LLMs
EchoDistill applies noisy-to-clean self-distillation with GRPO to boost Audio LLM robustness, reporting 4.18% average GSR gains under strong noise.
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ROGLE: Robust Global-Local Alignment with Automated Region Supervision for Text-Based Person Search
ROGLE introduces automated pseudo region-sentence pairs via RSM and multi-granular learning to boost fine-grained alignment in text-based person search, plus the P-VLG benchmark with over 100k annotated regions.
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DiscussLLM: Teaching Large Language Models When to Speak
DiscussLLM introduces a two-stage synthetic data pipeline to annotate multi-turn discussions with five intervention types and trains LLMs to time contributions via a silent token or proactive responses.