arXiv subjects

Kartik Ravisankar

Publications and source records attributed to Kartik Ravisankar.

4 recordsLinked to original sources

arXiv2026

Autoresearch for Marketplace Catalogs: From Legacy Forms to AI-Native Matching

Two-sided service marketplaces are moving from deterministic request-form intake to AI-native probabilistic matching, enabled by large language models (LLMs) that infer intent, preferences, and latent constraints from natural language. Relying on inferred intent rather than fixed-form fields forces these platforms to regenerate the provider-side preference taxonomy underwriting matching, search, and pricing: attributes interpretable to service providers while remaining a useful signal for marketplace decisions. We present an autoresearch loop that generates this taxonomy, one occupation at a time, and has been deployed in production at a major U.S. consumer services marketplace since April 2026, spanning 132 occupations. Instead of one global hierarchy, the loop treats each occupation as an independent generation problem and runs iterative propose-evaluate-keep refinement cycles. Each candidate tag set is scored by a recalibrated six-rubric LLM-as-judge framework, and a 7-critic panel of distinct personas contributes weighted penalties to an adjusted score, with no hard vetoes. A separate parity-mapping stage maps legacy request-form Q&A pairs back to the generated taxonomy, yielding both a coverage signal and an interface for human quality assurance; it does so by first inferring the provider attribute each legacy question was meant to measure, rather than translating questions to tags literally.

cs.AI↗

arXiv2026

Evaluating Enterprise Analytics Agents: An End-to-End, Trace-Backed Methodology

Enterprise analytics agents are not only text-to-SQL systems. They interpret business intent and choose metric definitions. They select data sources, execute tools, inspect results, and produce natural-language answers. Those answers may influence operational, financial, or executive decisions. Grading final answers hides where these agents fail. A plausible answer can use the wrong source of truth. It can skip a required decomposition, claim causality without support, or change its table interpretation across repeated runs. We present an end-to-end evaluation methodology for analytics agents. The methodology grades agent behavior across three families: semantic understanding, execution quality, and reliability. Grading uses question banks with human-written golden answers, repeated runs, and runtime traces. Each run first passes a run-validity check, then receives tiered, abstention-aware scores that feed a decision framework rather than a release gate. We instantiate the methodology on a controlled internal analytics agent at a large online marketplace. The case study uses 50 analytics questions, two anonymous model configurations, and three randomized repetitions per configuration, yielding 300 traces. The higher-capability configuration reduced early refusal from 73% to 0% and increased real-data answers from 21% to 73%. However, it also exhausted the tool-round budget on 16% of runs. It overran the schema-exploration budget on 77% of traces. It changed its table interpretation on 41 of 50 questions. On finance questions with structured golden answers, source table use and escalation improved, but canonical decomposition remained weak in both configurations. These results show why trust in analytics agents requires end-to-end, trace-backed evaluation rather than SQL correctness or final answer quality alone.

cs.SE↗

arXiv2026

Can you map it to English? The Role of Cross-Lingual Alignment in Multilingual Performance of LLMs

Large language models (LLMs) can answer prompts in many languages, despite being trained predominantly on English; yet, the mechanisms driving this generalization remain poorly understood. This work asks: How does an LLM's ability to align representations of non-English inputs to English impact its performance on natural language understanding (NLU) tasks? We study the role of representation alignment in instance-level task decisions, complementing prior analyses conducted both at the language level and task-independently. We introduce the Discriminative Alignment Index ($\DALI$) to quantify instance-level alignment across 24 languages other than English and three distinct NLU tasks. Results show that incorrect NLU predictions are strongly associated with lower representation alignment with English in the model's middle layers. Through activation patching, we show that incorrect predictions in languages other than English can be fixed by patching their parallel English activations in the middle layers, thereby demonstrating the causal role of representation (mis)alignment in cross-lingual correctness.

cs.CL↗

arXiv2025

Token-level Ensembling of Models with Different Vocabularies

Model ensembling is a technique to combine the predicted distributions of two or more models, often leading to improved robustness and performance. For ensembling in text generation, the next token's probability distribution is derived from a weighted sum of the distributions of each individual model. This requires the underlying models to share the same subword vocabulary, limiting the applicability of ensembling, since many open-sourced models have distinct vocabularies. In research settings, experimentation or upgrades to vocabularies may introduce multiple vocabulary sizes. This paper proposes an inference-time only algorithm that allows for ensembling models with different vocabularies, without the need to learn additional parameters or alter the underlying models. Instead, the algorithm ensures that tokens generated by the ensembled models \textit{agree} in their surface form. We apply this technique to combinations of traditional encoder-decoder models and decoder-only LLMs and evaluate on machine translation. In addition to expanding to model pairs that were previously incapable of token-level ensembling, our algorithm frequently improves translation performance over either model individually.

cs.CL↗