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Lee Mosbacker

Publications and source records attributed to Lee Mosbacker.

2 recordsLinked to original sources

Change the Funnel, Not Who May Submit: Engineering Peer Review for the Flood

Every reviewer knows the feeling: more papers each cycle, three full reviews for each of them, and a suspicion that near the accept line the outcome is luck. The numbers bear it out. NeurIPS received 30,709 submissions in 2026, more than three times as many as in 2021, and reviewing them took some 184,000 expert hours, or 92 years of researchers' time. Three-quarters of those hours went to papers that were rejected. And when NeurIPS twice had the same papers reviewed by two committees, the second rejected half of what the first accepted. Conferences are responding by limiting who may submit. We simulated that answer, and an alternative, in a model calibrated to the NeurIPS experiments. The limits save little and select no better, and those based on a past acceptance fall mostly on new authors. The alternative changes how papers are judged, spending full reads only where a decision turns on them. In a staged funnel, an automated quality check first turns back papers that are not ready. The rest are ranked by their value, from short summaries, by readers that each submission brings. Only the finalists get a full read, which checks their claims and settles the final ranking. The staged funnel needs a little over half of today's expert hours, accepts 95.5% of papers in the top 3-10% (against 74% today), and makes nearly every top-0.5% paper a spotlight or oral. And because every paper brings its own reader, the funnel keeps pace with the flood.

cs.LG↗

The Architecture of Errors: From Universal Impossibility to Patch-Local LLM Reliability

Universal LLM reliability is not a finite-library problem: across all possible tasks, tools, schemas, knowledge sources, and evaluator expectations, new intervention-distinguishable failure modes can appear without bound, so no finite intervention dictionary can guarantee bounded residual error for every such mode. But deployed systems do not operate over the whole universe. They operate inside operationally bounded patches (legal review, medical RAG, code repair, customer-support agents, contract extraction) with recurring tasks, schemas, tools, and evaluator expectations. Within such patches, empirical evidence suggests failures are sparse, repetitive, and concentrated in a small recurring catalogue, so reliability becomes a local catalogue-discovery and intervention-coverage problem rather than an exponential token-length problem. We formalize this transition with two propositions and one corollary. Proposition 1 is the worst-case-mode-wise negative result: no finite intervention dictionary covers every distinguishable failure mode of an unbounded domain. Corollary 1 is the inverse-discovery implication: the logarithmic upper bound on mode discovery cannot accommodate linearly more distinct tail modes without exponentially more observed hard-failure events. Proposition 2 is the positive patch-local result: under log active-mode exposure and head-heavy coverage, a sufficient per-hard-decision intervention budget grows polylogarithmically in sequence length and becomes domain-constant once the patch catalogue saturates. The framework relocates rather than dissolves long-context difficulty: where the number of hard decisions itself grows with task length, reliability remains hard; the contribution is to identify the on-axis intervention rather than to make those regimes easy.

cs.CL↗