Finding a relevant paper is not the same as producing a verifiable answer from it. LitTraceQA requires canonical paper identifiers, exact evidence at the page or object level, and typed answers that match the evaluator. We call the separation between source access and scorer-visible correctness the grounding contract gap. TRACE - Target-Aware Retrieval, Attributed Evidence, and Contract-Constrained Extraction - addresses this gap with target-grouped retrieval, independent typed evidence localization, multimodal table extraction, schema-driven table construction, and fail-closed validation. It indexes 27,487 papers through passage, object, alias, citation, and dense representations while retaining the question target behind each signal. For tables, TRACE predicts the observation unit before extracting values and assembles rows with evaluator-compatible key normalization. Our audited selected clean-track artifact scores 0.760613 on the official 71-question test set, including 0.9728 paper F1, 0.6847 evidence F1, 0.9800 multiple-choice accuracy, 0.5423 table-row F1, and 0.3508 macro cell accuracy. On 11 public-development table records, a clean baseline and coordinate-aware visual fill obtain row F1 of 0.291 and 0.411, respectively; this diagnostic comparison includes fallback outputs and is not an official-test claim. Remaining errors chiefly concern locator, observation-unit, row-key, and source-value identity.

Generic ham/spam labels do not define a financial-fraud blocking policy. We present a 1,393-message policy-aligned Indian SMS benchmark in Hindi, Hinglish, and English-India, separating source labels, fraud/non-fraud/unclear adjudication, and allow/review/block actions. GPT-4o and Gemini helped adjudicate source-positive messages, so positive metrics are secondary. Our primary endpoint is false-positive rate (FPR) on 1,294 policy-negatives: 1,222 untouched source negatives plus 72 source positives converted to safe. Under a common minimal prompt, six LLM APIs exceed illustrative 1% FPR gates on Bank and UPI templates. GPT-4o-mini records 77.3% and 56.9% FPR; a fine-tuned 270M XLM-R records 0.0% on both, 0.4% globally, and 0.980 recall. Policy definitions and seven examples reduce GPT-4o to 0.0% Bank/UPI and 1.5% global FPR at 0.949 recall; Claude reaches 1.8% at 0.960 recall. Prompting changes which gate fails. GPT-4o P3 and the AND consensus meet the FPR gates at lower policy-label recall; the local model meets both FPR and recall gates on this benchmark. Because positive labels partly use GPT-4o/Gemini, recall and F1 are secondary, not an overall ranking. We release labels, prompts, parsed decisions, analysis code, and checkpoints without a production or unseen-institution claim.

Slide narration generation can introduce unsupported visual or decorative claims. We describe a pipeline that indexes slide content with deterministic identifiers, generates an internally cited template, and applies heuristic checks before and after an optional language-model rewrite. A narration-stage study covers 30 publicly sourced presentations (279 slides). The same decks and procedure were used, but stochastic vision evidence was regenerated by condition. In one run per condition, the complete configuration produced 5/279 proxy flags (1.79%) versus 22/279 (7.89%) for A1, a 77.3% relative difference (slide-paired exact p ≈ 2.2 × 10⁻⁴; deck-blocked p = 0.0156); B produced 20/279 (7.17%). Saved labels mark 225/279 condition-C outputs as template fallbacks. Post-rewrite verdicts were not saved, and six outputs retained model narration when no nonempty verified template was available. The proxy has F1 = 0.60 against judgments based on extracted context rather than rendered slides. These are observed system-level proxy rates, not component attribution or evidence that hallucination is eliminated.

Fluent LLM explanations may not follow the evidence from a structured system. We present VERITYGATE, a four-gate checker for declared evidence IDs, entities, numbers, and claim types. It checks a fixed schema; it does not verify every fact in the prose. At r=0 and r=1, we test 900 instances per setting (450 grounded–ungrounded pairs) with GPT-4o-mini, Llama-3.3-70B, and Claude Sonnet 4.6. Under this schema-level contract and before repair, 80.3% of mini claims and 47.9% of Sonnet claims fail. These are verifier rejection rates, not prose-hallucination rates. One repair pass raises claim survival from 19.7% to 28.0% for mini and from 52.1% to 54.3% for Sonnet. Verified claims per example change by +0.14 for mini, −0.71 for Llama, and −0.47 for Sonnet, so survival and output volume must be reported together. A second Sonnet pass gives no clear gain. At r=1, Gate 4 covers 97.0%, 98.7%, and 100% of failing claims for mini, Llama, and Sonnet. Small human studies support the rules but show gaps between schema checks and correct prose. A domain-specific GPT-4o judge test shows an order effect, so it is only a usefulness check. We release the code and data.

EVICALC, our system for the DocSem shared task, achieved 8.61% joint accuracy on 1,730 tasks in the official final test evaluation. It reads a PDF, selects a passage, asks a language model to write an arithmetic expression, and evaluates that expression in local code. Saved intermediate results support inspection of failures. A separate public-validation run achieved 92.17% answer accuracy and 1.00 evidence F1. The configurations and metrics differ, so these scores are not a controlled comparison. Our manual, post-hoc analysis is descriptive: in one inspected case, optical character recognition (OCR) and block grouping merged the relevant passage into another block, and the system answered from unrelated text. An exploratory study of reading page images on 100 documents returned evidence identifiers for only 22 documents. These descriptive findings motivate further evaluation; they do not establish the causes of the overall score.