CAPS is a four-stage inference-only cascade that adapts how much of each solution the verifier sees and how comparisons are distributed, halving per-candidate verifier tokens while outperforming uniform pairwise verification on most benchmarks.
Position: Human-centric ai requires a minimum viable level of human understanding
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
PathCal calibrates reasoning paths by type-aware soft rebalancing of reflection-marker logits at uncertain states, yielding better efficiency-performance trade-offs on six benchmarks.
Agentic entropy names the systemic drift in AI coding agents away from architectural intent; a new framework using conformity seeding, reasoning monitoring, and causal graph interfaces supplies process-level oversight to complement existing review methods.
citing papers explorer
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CAPS: Cascaded Adaptive Pairwise Selection for Efficient Parallel Reasoning
CAPS is a four-stage inference-only cascade that adapts how much of each solution the verifier sees and how comparisons are distributed, halving per-candidate verifier tokens while outperforming uniform pairwise verification on most benchmarks.
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PathCal: State-Aware Reflection-Marker Calibration for Efficient Reasoning
PathCal calibrates reasoning paths by type-aware soft rebalancing of reflection-marker logits at uncertain states, yielding better efficiency-performance trade-offs on six benchmarks.
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Beyond the 'Diff': Addressing Agentic Entropy in Agentic Software Development
Agentic entropy names the systemic drift in AI coding agents away from architectural intent; a new framework using conformity seeding, reasoning monitoring, and causal graph interfaces supplies process-level oversight to complement existing review methods.