arXiv subjects

Jayanaka Dantanarayana

Publications and source records attributed to Jayanaka Dantanarayana.

4 recordsLinked to original sources

arXiv2026

SIGIL: Skill Compilation for Reliable and Efficient Agent Execution

Agent skills describe reusable procedures, but runtime models must still interpret their instructions and coordinate execution. We introduce Skill Compilation, which translates the procedure prescribed by an authored skill into an executable harness while preserving decisions left to the model. Our compiler, SIGIL, translates skills and their resources into a typed intermediate representation, validates it, and deterministically generates the harness. We evaluate SIGIL on 11 compliance-critical skills, where following the prescribed procedure is part of correctness. SIGIL improves adherence across all four runtime models, achieving up to 100% measured mean Skill Adherence compared with 26.2-54.0% for direct SKILL.md execution. On this suite, SIGIL reduces total runtime tokens by 21-45% for three of the four models. On SkillsBench, SIGIL also improves task performance across all four models, with absolute gains of 3.5-35.7 percentage points. These findings suggest that compiling reusable skill procedures into executable harnesses can improve adherence, task completion, and runtime efficiency.

cs.SE↗

arXiv2026

Automated Prefetching for Object Spatial Programming Using Temporal Trace Graphs

Dependent database accesses can serialize application--database round trips on a request's critical path. Planning across traversal steps requires knowing both which relationships a computation may follow and how discovered objects become subsequent execution contexts. We identify a programming-model contract that exposes this information through inspectable access paths and explicit traversal continuation. We demonstrate this contract in Object-Spatial Programming (OSP), where walkers traverse a typed object graph and execute node-type-specific code. We introduce the Temporal Trace Graph (TTG), a bounded, request-specific graph of candidate objects and traversal-derived transitions. At walker spawn, the TTG Builder composes statically extracted access and continuation rules over the live graph from the request's starting node. An asynchronous prefetcher loads planned objects while the walker executes. We implement TTG in Jac/PostgreSQL and evaluate four applications covering linear, multi-hop, recursive, and value-dependent traversals. TTG improves end-to-end latency in three applications, achieving up to 2.99x speedup over the stock runtime. Comparisons with manually staged prefetching characterize the benefits and costs of automatic cross-step planning. The evaluation also shows that planning overhead can exceed avoided demand latency and that value-dependent control can introduce bounded overfetch.

cs.DB↗

arXiv2026

GraphMend: Code Transformations for Fixing Graph Breaks in PyTorch 2

This paper presents GraphMend, a compiler technique that automatically fixes FX graph breaks in PyTorch 2 programs. Although PyTorch 2 introduced TorchDynamo and TorchInductor to enable just-in-time graph compilation, certain code patterns still cause graph breaks that force execution to fall back to Python eager mode, introducing costly CPU-GPU synchronization and reducing optimization opportunities. Our investigation of 195 Hugging Face models reveals that 13.8% of models exhibit graph breaks. GraphMend automatically eliminates fixable breaks through source-level program analysis and transformations. It analyzes AST-level program structure to identify graph-break patterns and applies transformations only when their semantic preservation can be statically established. These transformations enable PyTorch to capture larger, uninterrupted FX graphs without manual refactoring by developers. We evaluate GraphMend on all 27 models found to exhibit graph breaks in our investigation. GraphMend eliminates 107 of 147 graph breaks (73%), fully fixing all breaks in 21 models. In our experiments on NVIDIA GPUs, GraphMend achieves up to 26x cold-start speedup, 5x on average, and up to 1.39x steady-state forward pass speedup. These results demonstrate that semantics-aware source-level analysis and transformation are effective complements to PyTorch's dynamic JIT compilation pipeline, substantially improving both usability and performance.

cs.PL↗

arXiv2024

Scaling Down to Scale Up: A Cost-Benefit Analysis of Replacing OpenAI's LLM with Open Source SLMs in Production

Many companies use large language models (LLMs) offered as a service, like OpenAI's GPT-4, to create AI-enabled product experiences. Along with the benefits of ease-of-use and shortened time-to-solution, this reliance on proprietary services has downsides in model control, performance reliability, uptime predictability, and cost. At the same time, a flurry of open-source small language models (SLMs) has been made available for commercial use. However, their readiness to replace existing capabilities remains unclear, and a systematic approach to holistically evaluate these SLMs is not readily available. This paper presents a systematic evaluation methodology and a characterization of modern open-source SLMs and their trade-offs when replacing proprietary LLMs for a real-world product feature. We have designed SLaM, an open-source automated analysis tool that enables the quantitative and qualitative testing of product features utilizing arbitrary SLMs. Using SLaM, we examine the quality and performance characteristics of modern SLMs relative to an existing customer-facing implementation using the OpenAI GPT-4 API. Across 9 SLMs and their 29 variants, we observe that SLMs provide competitive results, significant performance consistency improvements, and a cost reduction of 5x~29x when compared to GPT-4.

cs.SE↗