Papers › ECCO: Can We Improve Model-Generated Code Efficiency Without Sacrificing Functional...

ECCO: Can We Improve Model-Generated Code Efficiency Without Sacrificing Functional Correctness?

19 Jul 2024arXiv:2407.14044archive 2025-07-28

Siddhant Waghjale, Vishruth Veerendranath, Zora Zhiruo Wang, Daniel Fried

Although large language models (LLMs) have been largely successful in generating functionally correct programs, conditioning models to produce efficient solutions while ensuring correctness remains a challenge. Further, unreliability in benchmarking code efficiency is a hurdle across varying hardware specifications for popular interpreted languages such as Python. In this paper, we present ECCO, a reproducible benchmark for evaluating program efficiency via two paradigms: natural language (NL) based code generation and history-based code editing. On ECCO, we adapt and thoroughly investigate the three most promising existing LLM-based approaches: in-context learning, iterative refinement with execution or NL feedback, and fine-tuning conditioned on execution and editing history. While most methods degrade functional correctness and moderately increase program efficiency, we find that adding execution information often helps maintain functional correctness, and NL feedback enhances more on efficiency. We release our benchmark to support future work on LLM-based generation of efficient code.

PaperPDFCodeCode Syntology ran

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2407.14044")

Code

Syntology Ran 10 of 14 code samples harvested from 2 repositories linked to this paper; 4 have no recorded run. Of those that ran: 10 ran with no contract checked.

By repository: official repository: 5 samples from 1 repository, 3 ran; found in paper text by Syntology: 9 samples from 1 repository, 7 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

codeeff/ecco officialmentioned in paper report
judge0/judge0 mentioned on GitHubGPL-3.0 report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

14 samples harvested; 10 ran; 0 honoured the contract we drafted; 4 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

10ran
4unverified

Licence: 5 of the 14 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from 2 repositories linked to this paper, official or community; each sample names its own and says which. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

build_coder_prompts codeeff/ecco/experiments/utils.py official repository ran no licence file found · pointer only · ffe9c8c7bc502548 · report
build_feedback_prompts codeeff/ecco/experiments/utils.py official repository ran no licence file found · pointer only · 882b572136753409 · report
get_execution_feedback codeeff/ecco/experiments/utils.py official repository ran no licence file found · pointer only · 3e1be7ab52fa3b07 · report
judge_eval_single_test codeeff/ecco/evaluation/utils.py official repository unverified no licence file found · pointer only · 3f906c29f0acb355 · report
judge_submit codeeff/ecco/evaluation/utils.py official repository unverified no licence file found · pointer only · 10835a1ccca19d51 · report
apply_scores_before_experts_if_enabled microsoft/DeepSpeed/deepspeed/module_inject/auto_ep_layer.py found in paper text by Syntology ran Apache-2.0 (permissive) · bc9117fa9c34b5ef · report
autoep_folding_gradient_reduction_strategy microsoft/DeepSpeed/deepspeed/module_inject/auto_ep_folding.py found in paper text by Syntology ran Apache-2.0 (permissive) · 30700c5fc6cd1064 · report
expected_folding_group_tables microsoft/DeepSpeed/deepspeed/module_inject/auto_ep_folding.py found in paper text by Syntology ran Apache-2.0 (permissive) · ad8cfe379b811c40 · report
human_readable_size microsoft/DeepSpeed/deepspeed/env_report.py found in paper text by Syntology ran fingerprinted Apache-2.0 (permissive) · 66a3320fa7fe659e · report
local_folding_ranks microsoft/DeepSpeed/deepspeed/module_inject/auto_ep_folding.py found in paper text by Syntology ran Apache-2.0 (permissive) · 5f8a25a43a552f16 · report
move microsoft/DeepSpeed/deepspeed/module_inject/auto_tp.py found in paper text by Syntology ran Apache-2.0 (permissive) · 7a2e41bee4f05c5a · report
resolve_combine_impl microsoft/DeepSpeed/deepspeed/module_inject/auto_ep_layer.py found in paper text by Syntology ran fingerprinted Apache-2.0 (permissive) · a2da1bc94fa8af7d · report
get_annotations microsoft/DeepSpeed/deepspeed/compat.py found in paper text by Syntology unverified Apache-2.0 (permissive) · 45604a14e400b2f3 · report
get_annotations_from_namespace microsoft/DeepSpeed/deepspeed/compat.py found in paper text by Syntology unverified Apache-2.0 (permissive) · 8c9ef2f97041c68b · report

Tasks

BenchmarkingCode GenerationIn-Context Learning

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections