Customer-obsessed science
Research areas
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August 26, 20265 min readDiscounting the opinions of LLM judges with highly correlated outputs ensures that panels of judges reflect a true diversity of perspectives.
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August 21, 20269 min read
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July 30, 20268 min read
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July 9, 202610 min read
Featured news
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2026Recent progress in multi-turn reinforcement learning (RL) has significantly improved reasoning LLMs' performances on complex interactive tasks. Despite advances in stabilization techniques such as fine-grained credit assignment and trajectory filtering, instability remains pervasive and often leads to training collapse. We argue that this instability stems from inefficient exploration in multi-turn settings
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2026Vision language models face a fundamental geometry trade-off: Euclidean representations excel at instance-level discrimination, while hyperbolic representations naturally encode semantic hierarchies. Hybrid training is challenging because one geometry may dominate early, leaving the other under-trained failure mode we term geometry dominance. We introduce Adaptive Geometry Routing (AGR), a framework that
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Interspeech 20262026Audio encoders are critical to modern audio applications as large language models (LLMs) increasingly rely on a single encoder for diverse inputs. While self-supervised learning (SSL) has yielded strong domain-specific encoders like speech or music experts, multi-domain approaches like USAD and SPEAR remain limited in coverage and evaluation. Recent studies also suggest supervised encoders align better
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ACM DocEng 20262026Operational runbooks increasingly function as living documents within operational workflows: they are maintained by people, used in incident support, and continuously revised as organizational knowledge changes. Yet little is known about how such document collections evolve over time in production settings, or which interpretable signals are useful for monitoring document change. We analyze 17 weeks of
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2026Multi-agent systems (MAS) are increasingly capable of tackling complex real-world tasks, yet their reliance on inter-agent coordination, tool use, and long-horizon reasoning makes error recognition particularly challenging. Minor errors can propagate across agents, escalating into task failures while producing long, intertwined execution trajectories that impose significant costs for both human developers
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