The result is an auditable division of labor: models widen the search for removable code, order bounds the damage a mis-ranked proposal can do, and execution retains authority over every committed deletion.
Abstract
Frontier coding models now match or exceed strong human reference points on programming benchmarks, yet benchmark success does not imply maintainable software. Prompt-driven"vibe coding"is additive: new branches, guards, and fallbacks accumulate faster than obsolete logic is removed. We study the inverse problem-how an Al system should remove code when execution-verification capacity is finite. We formulate redundant-code reduction as proposal scheduling: a ranker orders single-statement deletion candidates, an execution suite accepts the first candidate that passes, and a budget bounds how many candidates may be tested. Our central observation is that candidate order, not model confidence, is the control surface a deployment can reason about. DELSCOUT instantiates two schedules. Given representative target-domain validation, a five-slot budget spends three slots on deterministic shortest-first candidates and two on complementary learned candidates; across nine MBPP replications with 0.5B, 0.6B, and 8B rankers this raises verified-deletion coverage by 9.5% relative (+6.7 accepted tasks) while consuming slightly fewer verifier calls than the matched static baseline. Without such validation the same rankers can lose coverage under shift, so we instead evaluate the complete static prefix first and append learned candidates only afterwards; for a deterministic verifier this makes coverage and character reduction non-decreasing by construction, at a measured 4.8-62.5% increase in verifier calls. MBPP+ then erases the in-domain advantage, showing that scheduling governs search while the test suite alone governs what"preserving behavior"means. The result is an auditable division of labor: models widen the search for removable code, order bounds the damage a mis-ranked proposal can do, and execution retains authority over every committed deletion.
The results show that executable feedback can repair secure-code generation, but its benefits depend on the model, task, feedback entry point, and especially test coverage.
Yun-Hao Liang, Cheng-Guang Gan, Rui-Xuan Ying et al.· 1 citation
Performance modeling is central to hardware design and software optimization, yet constructing these models requires structured reasoning about computation, data reuse, storage, and movement. We introduce PerfReasoning, a benchmark that evaluates LLMs both as direct performance reasoners and as generators of analytical performance-model code. Given workload, architecture, and mapping specifications, models compare mappings and predict off-chip traffic and buffer requirements. The strongest closed-source models exceed 90% on reasoning-based Q&A, and the best open-weight model reaches 82.4%. However, model construction is substantially harder: while GPT-5.6 Sol exceeds 80% pass rate, all other model configurations average below 15% and vary markedly across runs. Task-specific RL raises a 4B model's mapping-reasoning accuracy by 15.7 points, whereas feedback-free multi-round self-revision prompting is not reliably effective. PerfReasoning exposes the gap between plausible architectural reasoning and reliable performance-model construction. We will publicly release the benchmark to support reproducible evaluation and track future progress.
Da Zhao, K. Sankaralingam, Christos Kozyrakis et al.· 0 citations
Standard Delay Format (SDF)-annotated switching simulation distinguishes delay-dependent activity among functionally equivalent rewrites, but evaluating every candidate repeats timing, compilation, and replay. A zero-delay proxy can remove timed evaluations, but generating that proxy candidate by candidate can cost more than the timed work it saves. We present Batch Before You Time (BBYT), which compiles all candidates of one rewrite decision into one scoped zero-delay image and either commits a well-separated proxy winner or invokes the unchanged timed chain. Across 12 counterbalanced holdout sequences, BBYT reduces complete candidate-selection time by 18.05% on average; the design-level reductions are 5.84% and 30.25%, with both confidence intervals above zero. On a counterbalanced 8,192-transition C6288 workload, BBYT is 9.24% faster than the same gate executed with candidate-wise proxy launches. In the five-workload corpus, BBYT removes 32.52% of timed candidate evaluations and matches exhaustive timed selection on all 250 evaluated decisions. For on-demand timing-aware rewrite selection, proxy execution and fidelity continuation should use the same decision scope.
This work presents Evolutionary Self-Debugging Agents (ESDA), which mines tool traces into structured failure signatures and uses them to maintain a strategy bank of reusable debugging policies, and analyze transfer across languages and build systems and finds that mining failure signatures yields consistent gains under distribution shift.
Shuang Cao, Rui Li· Proceedings of the 32nd ACM...· 0 citations
Hardware engineering exposes coding agents to a form of long-horizon work that is difficult to capture with pass-at-k: progress is continuous, tool feedback is delayed and heterogeneous, and a backend failure may require revising RTL rather than tuning another physical-design parameter. Existing benchmarks measure RTL generation, repository repair, verification, PPA evolution, or physical implementation, but their different designs and oracles make it hard to determine where an agent succeeds or fails across abstraction boundaries. We introduce CLOSER-Bench, a controlled evaluation protocol for budgeted cross-stage design closure. For one design and one hidden objective, it pairs spec-to-RTL, RTL-to-GDS, and spec-to-GDS tasks, records every simulator, synthesis, STA, and place-and-route invocation, and measures final quality, anytime progress, tool cost, and cross-stage recovery. The benchmark is built on open-source Verilator, Yosys, OpenROAD, KLayout, Sky130, and the Harbor agent harness. A ten-task pilot spanning RTL repair, mutation-based verification, coverage, PPA optimization, design-space exploration, cross-model debugging, and security establishes the executable harness and exposes a sharp completion--closure gap: three agents solve a localized AXI repair task, while the matched verification-closure task separates a frontier agent from two otherwise successful baselines. We further validate a full RTL-to-GDS flow and construct a macro-based AXI/DMA streaming accelerator for the stage-paired evaluation. These results motivate treating hardware closure as a budgeted sequential decision problem rather than a collection of independent code generation tasks.
Peilong Zhou, Zhirong Chen, Cangyuan Li et al.· arXiv.org· 0 citations
Overall, TraceGate shows that rethinking debugging through controlled observability, rather than relying solely on stronger models or larger prompts, can make LLM-assisted repair more effective, efficient and controllable.
Nicolas Schuler, MateVincenzoScotti, RaffaelaMirandola· 0 citations
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