arXiv subjects

Aditya Dutta

Publications and source records attributed to Aditya Dutta.

3 recordsLinked to original sources

arXiv2026

Train Often, Deploy Selectively: Forward-Gated Model Replacement in Crypto Markets

Production forecasting systems retrain models regularly, but a retrained candidate does not necessarily outperform a continuously maintained incumbent that has continued to learn. We introduce Shadow Before Swap (SBS), a deployment policy that warm-refits a challenger off the serving path, evaluates it against the maintained incumbent on the same next week of delayed labels, and promotes it only after a fixed paired negative-log-likelihood (NLL) advantage. In historical replay over two nonoverlapping Binance episodes spanning 48 UTC weeks, three seeds, eight underlyings, and two perpetual-futures contract types, SBS reduces NLL by 0.1472% relative to calendar replacement, 0.0755% relative to schedule-matched automatic promotion, and 0.0428% relative to continuous maintenance. The corresponding episode-stratified four-week block intervals are 0.1139%-0.1754%, 0.0521%-0.0980%, and 0.0301%-0.0554%, respectively. SBS promotes 114 of 528 challengers, reducing deployed model changes by 78.4% while improving the serving trajectory. The effect remains directionally consistent across seeds, trial budgets, promotion margins, an earlier 20-asset panel, and a topology-matched supervised objective. SBS thus provides a practical deployment policy that improves probabilistic forecasts while limiting consequential model-state transitions.

cs.CE↗

arXiv2026

Stigmergic Graph Memory: An Environment-Aware Approach for Many-to-Many Multi-Agent Pickup and Delivery

Automated fulfillment warehouses must continuously assign and execute pickup-and-delivery work while avoiding congestion. In many-to-many Multi-Agent Pickup and Delivery (MAPD), a request specifies a stock-keeping unit rather than fixed endpoints, requiring the controller to select an agent, source, and destination before path planning. Existing graph-guidance methods primarily influence routing after goals are fixed, leaving endpoint instantiation uninformed by recent traffic. We introduce Stigmergic Graph Memory (SGM), a bounded, decaying memory layer that records recent execution signals on warehouse nodes and directed edges to rank feasible endpoints and route preferences without altering collision constraints or planner validity. Across paired request streams on five layouts, three load levels, and 25 seeds per condition, SGM outperforms two reconstructed many-to-many allocation baselines in all 15 map-load conditions, with paired throughput gains of 20.5-36.7%. These results show that recent execution memory can improve warehouse throughput by shaping which feasible goals enter the planner, not only how agents travel to already fixed goals.

cs.MA↗

arXiv2026

LLMs as Assessors: Right for the Right Reason?

A good deal of recent research has focused on how Large Language Models (LLMs) may be used as judges in place of humans to evaluate the quality of the output produced by various text / image processing systems. Within this broader context, a number of studies have investigated the specific question of how effectively LLMs can be used as relevance assessors for the standard ad hoc task in Information Retrieval (IR). We extend these studies by looking at additional questions. Most importantly, we use a Wikipedia based test collection created by the INEX initiative, and prompt LLMs to not only judge whether documents are relevant / non-relevant, but to highlight relevant passages in documents that it regards as useful. The human relevance assessors involved in creating this collection were given analogous instructions, i.e., they were asked to highlight all passages within a document that respond to the information need expressed in a query. This enables us to evaluate the quality of LLMs as judges not only at the document level, but to also quantify how often these judges are right for the right reasons. Our observations lead us to reiterate the cautionary note sounded in some earlier studies when it comes to using LLMs as assessors for creating IR datasets: while LLMs are unquestionably promising, and may be used judiciously to subtantially reduce the amount of human involvement required to generate high-quality benchmark datasets, they cannot replace humans as assessors.

cs.IR↗