Research

Asks when conformal risk control can place a distribution-free guarantee on an LLM output, and proves when it cannot. Heuristic abstention rules miss their stated risk targets by 15 to 40%, while adaptive conformal inference cuts cross-dataset coverage violations from 71% to 21%. The paper adds an impossibility result, a strict hierarchy of bounds, and a closed-form feasibility test for whether a chosen risk target is even reachable.

Sole-authored.

A retrieval-augmented generation approach for closed, domain-specific question answering that uses user interaction signals to improve retrieval and reduce ungrounded answers. It underpins production question answering at Adobe and has been built on by independent academic and industry groups.

Co-author (one of eight). Cited 50+ times.

Vision-language models often answer confidently while relying on the wrong evidence. EVICT tests this directly: it masks the image region the model claims to depend on, then re-runs the same question. If the answer does not change, the model was not actually using the evidence it cited, and the answer is flagged as unverified.

The probe needs no training and no ground-truth labels, so it is cheap to run as a reliability guardrail on top of an existing model. Its honest limitation: it detects evidence-independence, not correctness. An answer can be genuinely grounded and still wrong.

Sole-authored. Published at the CVPR 2026 GRAIL-V workshop.

In a multi-stage system (NER → disambiguation → typing) errors compound, so calibrating each stage alone under-covers while a Bonferroni union bound over-covers. PASC reduces joint coverage to a single conformal problem on the pipeline's maximum nonconformity score.

On a three-stage pipeline over CoNLL-2003 it reaches 96.4% end-to-end coverage versus 93.4% (Bonferroni) and 86.5% (independent calibration), at the same prediction-set size, and empirically holds target coverage under distribution shift, where independent calibration collapses to 59%.

Sole-authored.

Formalizes "format collapse," where one prompt yields clean JSON on one model and malformed output on another, and adds a canonicalization and verification layer so strict parsers stop rejecting correct extractions.

It repairs form, not meaning: it can rescue a malformed-but-correct extraction, but it will not catch a confidently wrong one.

Sole-authored.

Predicts, per query, whether a smaller model will succeed, and uses that calibrated forecast to escalate only the queries it is likely to fail. Because the forecast is calibrated, the routing threshold maps directly to a chosen cost/quality operating point. On a 75,000-query production named-entity workload, calibration cut expected calibration error from 0.12 to 0.03, and the resulting cascade reached 0.91 micro-F1 at 31% lower cost than always using the large model.

Sole-authored.

When LLMs annotate contested social and political text they fail in opposite directions at once, one model over-flags where another under-flags, and they can underestimate how much opposition a population holds by 24 to 40 points. Worse, aggregate accuracy can look near-perfect through “accidental cancellation” while both directional errors stay large.

Sole-authored.

Shows that rewriting a query before retrieval is strongly domain-dependent, it helped on TREC-COVID but hurt on FiQA, because rewrites that swap out domain-specific terms degrade queries that already matched well.

Sole-authored.

Standard NER benchmarks reuse the same entities across train and test, so a high score can reflect memorization rather than generalization. This measures how far those scores overstate performance once entity types recombine into novel, unseen compositions, the conditions production systems actually face, and argues for evaluation that reflects them.

Accepted for oral presentation at ANNPR 2026 (Springer proceedings); also presented as a poster at the ICML 2026 CompLearn workshop.

Sole-authored.

When you compare two models by their internal representations, which models you pick can drive the result. This selects architecture-homogeneous models so an alignment score reflects the representations themselves, not architectural confounds.

Sole-authored.