system architects

Abstract: Engineering multi-agent systems (MAS) requires choosing agents, orchestration strategies, communication mechanisms, reasoning approaches, and memory configurations. These choices depend on framework-specific capabilities and abstractions, requiring developers to learn different APIs when exploring alternative implementations. We present GEAR, an open-source tool for configuring MAS and generating framework-specific implementations from a common specification. GEAR uses a feature-based representation to make configuration options and their constraints explicit, enabling users to validate specifications before code generation. Through connectors for ten agentic frameworks, users generate projects from the same specification and can then inspect reports describing framework-specific adaptations. A preliminary assessment of mapping feasibility identifies translation strategies for 271 of 320 evaluated feature-framework pairs (84.69%). GEAR supports developers prototyping agentic systems and researchers exploring MAS configurations across frameworks. GEAR is available as open-source software at https://github.com/brellsanwouo/gear-framework and a video demo is available at https://youtu.be/iwNi-oC2tCw.

Abstract: Typed decision models (TDMs) are increasingly used in applications such as request routing and model selection, making reliable decisions critical to system performance. Fallback options accommodate categories outside regular choices, enabling deferral to human experts or more capable models. While recent works have examined adversarial vulnerabilities in TDMs, their ability to follow fallback rules in non-adversarial settings remains underexplored. We introduce the Paired Fallback Robustness (PFR) Test to evaluate two aspects of fallback reliability: whether models correctly switch between regular and fallback options, and whether they make consistent decisions under logically equivalent definitions of the same rule. We evaluate open-source decision models on three benchmarks, with controlled comparisons across architectures and parameter counts. We find that correct recognition does not reliably transfer to correct fallback options, and errors persist even when the correct category is explicitly supplied.Moreover, logically equivalent formulations of the same fallback rule can also produce substantial disagreement in model decisions. These findings reveal a crucial reliability gap in current TDMs: fallback mechanisms designed to improve reliability can instead introduce errors. It highlights faithful execution of fallback rules and consistency under equivalent definitions as essential requirements for reliable typed decision-making.

Abstract: Thermal constraints in heterogeneous multi-core systems can significantly degrade performance, as conventional techniques such as dynamic voltage and frequency scaling (DVFS) and task migration introduce overhead under high utilization. Approximate computing, particularly loop perforation, can mitigate this overhead by reducing computation at the cost of accuracy loss. However, existing approaches typically apply a uniform perforation rate across all loops, ignoring loop-specific sensitivity. We propose AMAX, an accuracy-aware multi-loop asymmetric perforation framework that reduces the combinational design space through loop-wise profiling and multi-objective configuration selection, and dynamically adapts the resulting configurations at runtime according to performance requirements. Experiments on PARSEC benchmarks show that {\em AMAX} achieves up to 0.85 improvement in normalized accuracy over symmetric perforation for the same performance targets.

Abstract: Large language models (LLMs) have achieved strong performance in mathematical and logical reasoning, yet they still suffer from the curse of complexity and struggle as problem difficulty increases with the number of constraint interactions and the size of the search space. In contrast, satisfiability solvers such as Z3 provide scalable and verifiable reasoning through constraint solving under formal theories, but lack the flexibility to interpret natural-language problem descriptions. In this work, we propose SAT-RL, a reinforcement-learning (RL) framework that trains LLMs to interact with symbolic solvers through multi-turn tool execution. SAT-RL leverages synthetic data generation with verifiable solutions, supervised warm-up on solver-augmented traces, and solver-grounded reinforcement learning with tool-aware rewards. Experiments show that SAT-RL substantially improves symbolic reasoning with tools on both in-domain and out-of-domain logic tasks. Our code is available on https://github.com/sunjia72/SAT-RL.

Abstract: Near Data Processing (NDP) has the potential to significantly improve system performance and energy by alleviating data movement bottlenecks. However, most NDP proposals pose heavy requirements for the software stack, data layout, and/or the underlying hardware. To broaden NDP adoption, this work focuses on a modular hardware-centric approach that automates the above steps and can target unmodified binaries. This paper presents Near Data Strands (NDS), a framework that orchestrates tasks and data automatically without involving the programmer and the software stack. As a first step towards this ambitious goal, this work focuses on regular loops that meet specific criteria. The framework identifies potential offloadable loops, decomposes them into parallel execution strands, creates a high performance instruction schedule, performs the required data marshaling and orchestration, and initiates near-data execution. We demonstrate that NDS achieves a 3.14x geomean speedup over a single-core host baseline and a 1.82x geomean speedup over 8-core host-parallel variants, all without any modifications to the software stack. These gains grow as repeated loop invocations amortize offload costs, showing that legacy applications can leverage a transparent hardware approach to extract benefits from NDP.