Debangshu Banerjee

Shaurya Gomber, Debangshu Banerjee, Gagandeep Singh

Current numerical abstract interpretation relies on fixed, hand-crafted, instruction-specific transformers tailored to each domain, causing three key limitations: transformers cannot be reused across domains; precise compositional reasoning over instruction sequences is difficult; and all downstream tasks must use the same fixed transformer regardless of precision or efficiency needs.To address this, we propose the Evolving Abstract Transformer, which replaces the fixed single-output design with an adaptable search over a parametric space of sound outputs via two algorithms. First, the Universal Parametric Output Space Encoder (UPOSE) constructs a compact parametric space of sound outputs for any polyhedral numerical domain and any operator in the Quadratic-Bounded Guarded Operators (QGO) class, covering both individual instructions and structured sequences. Second, the Adaptive Gradient Guidance (AGG) algorithm leverages the differentiable structure of UPOSE's output space to efficiently search it according to downstream objectives and available runtime, continually evolving the output as more time is provided. We implement these ideas in the AbsEvolve framework and evaluate across three numerical abstract domains: Zones, Octagons, and Polyhedra. Results show the evolving transformer works across domains and instructions, enables efficient precision-efficiency tradeoffs by adjusting number of gradient steps in the search, and reaches the most precise invariants up to 3.2x faster than existing baselines.

Tarun Suresh, Debangshu Banerjee, Shubham Ugare et al.

Diffusion LLMs have emerged as a promising alternative to conventional autoregressive LLMs, offering significant potential for improved runtime efficiency. However, existing diffusion models lack the ability to provably enforce user-specified formal constraints, such as regular expressions, which makes them unreliable for tasks that require structured outputs, such as fixed-schema JSON generation. Unlike autoregressive models that generate tokens sequentially, diffusion LLMs predict a block of tokens in parallel. This parallelism makes traditional constrained decoding algorithms, which are designed for sequential token prediction, ineffective at preserving the true output distribution. To address this limitation, we propose DINGO, a dynamic programming-based constrained decoding strategy that is both efficient and provably distribution-preserving. DINGO enables sampling of output strings with the highest probability under the model's predicted distribution, while strictly satisfying any user-specified regular expression. On standard symbolic math and JSON generation benchmarks, DINGO achieves up to a 68 percentage point improvement over unconstrained inference

Debangshu Banerjee

Hex is a complex game with a high branching factor. For the first time Hex is being attempted to be solved without the use of game tree structures and associated methods of pruning. We also are abstaining from any heuristic information about Virtual Connections or Semi Virtual Connections which were previously used in all previous known computer versions of the game. The H-search algorithm which was the basis of finding such connections and had been used with success in previous Hex playing agents has been forgone. Instead what we use is reinforcement learning through self play and approximations through neural networks to by pass the problem of high branching factor and maintaining large tables for state-action evaluations. Our code is based primarily on NeuroHex. The inspiration is drawn from the recent success of AlphaGo Zero.