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compute_entropy_loss

Syntologyentry name in harvested coderead from the graph 2026-09-24

compute_entropy_loss appears in the code Syntology harvested for 20 papers, as 8 distinct code bodies found in 21 places (a place is one code body under one paper). At least one of them ran in 16 of the papers; 2 of the code bodies carry a behaviour fingerprint.

What this page is not. Routines are grouped here by the exact string of their function or class name. Nothing asserts that two samples named compute_entropy_loss do the same thing, share code, or are comparable; the name is a string, not an identity. Behaviour outputs (what a fingerprinted sample returned on the shared battery) are not in this export and are not shown here; the graph at syntology.ai holds them. "Ran" means executed on a synthesized fixture, not that the code is correct or reproduces a paper.

Samples Syntology

Syntology ran 5 of the 8 distinct code bodies named compute_entropy_loss; 3 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
4ran · our draft was wrong
1ran · fixture could not drive it
0ran
3unverified
2fingerprinted

Licence is a property of each copy, so it is counted per place: 12 of the 21 places are pointer only (Syntology does not serve that copy's text). This site shows no code text for any sample; every row below links to the file in its repository where the record names one.

“Ran” means the sample executed on a synthesized input; it does not mean the output is correct. “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, and those samples did run. The ran count above is every status except unverified, the same rule as each paper page.

Papers

20 papers shown of 20, newest first; 21 places in the table. A paper with no recorded date is placed by the month its arXiv id encodes, shown in the Date column as YYYY-MM (from id). One row per place: a paper whose repository defines the name more than once appears more than once, and the same code body held for several papers appears once under each, with the same status. Titles and dates are the archive's archive 2025-07-28 for papers in the archive. Status and fingerprint are Syntology's record of each code body; licence is recorded for each place. The File cell ends with the code body's 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.

PaperDateFileStatus SyntologyLicence
Hita: Holistic Tokenizer for Autoregressive Image Generation 3 Jul 2025 CVMI-Lab/Hita/hita/tokenizer/tokenizer_image/vq_model.py 18f299635e1403d0 ran · our draft was wrong MIT (permissive)
GoT-R1: Unleashing Reasoning Capability of MLLM for Visual Generation with Reinforcement Learning 22 May 2025 gogoduan/got-r1/src/models/vq_model.py 774c95b77d7621f3 ran · fixture could not drive it no licence file found · pointer only
GigaTok: Scaling Visual Tokenizers to 3 Billion Parameters for Autoregressive Image Generation 11 Apr 2025 SilentView/GigaTok/tokenizer/tokenizer_image/vq/vq_vit_model.py a85cfd31cc2ac08c ran · our draft was wrong licence not identified · pointer only
Halton Scheduler For Masked Generative Image Transformer 21 Mar 2025 valeoai/halton-maskgit/Network/vq_model.py 774c95b77d7621f3 ran · fixture could not drive it MIT (permissive)
Autoregressive Image Generation with Randomized Parallel Decoding 13 Mar 2025 hp-l33/ARPG/models/vq_model.py 774c95b77d7621f3 ran · fixture could not drive it MIT (permissive)
OmniMamba: Efficient and Unified Multimodal Understanding and Generation via State Space Models 11 Mar 2025 hustvl/omnimamba/llamagen_tokenizer/tokenizer_image/vq_model.py 774c95b77d7621f3 ran · fixture could not drive it MIT (permissive)
FlexVAR: Flexible Visual Autoregressive Modeling without Residual Prediction 27 Feb 2025 jiaosiyu1999/FlexVAR/models/vq_llama.py 774c95b77d7621f3 ran · fixture could not drive it MIT (permissive)
PanoLlama: Generating Endless and Coherent Panoramas with Next-Token-Prediction LLMs 24 Nov 2024 0606zt/panollama/image_tokenizer/vq_model.py 774c95b77d7621f3 ran · fixture could not drive it no licence file found · pointer only
Scalable Autoregressive Image Generation with Mamba 22 Aug 2024 hp-l33/aim/models/stage1/vq_model.py 774c95b77d7621f3 ran · fixture could not drive it MIT (permissive)
Dungeons and Data: A Large-Scale NetHack Dataset 1 Nov 2022 identical code first harvested elsewhere 3fba94f2f8bc0565 unverified licence of this copy not recorded
Improving Policy Learning via Language Dynamics Distillation 30 Sep 2022 identical code first harvested elsewhere 3fba94f2f8bc0565 unverified licence of this copy not recorded
The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models 10 Jan 2022 aypan17/reward-misspecification/atari/torchbeast/monobeast.py a27a9dc97eefda7f ran · our draft was wrong fingerprinted MIT (permissive)
Interesting Object, Curious Agent: Learning Task-Agnostic Exploration 25 Nov 2021 identical code first harvested elsewhere 41c8773f66a31bbf ran · our draft was wrong fingerprinted licence of this copy not recorded
SILG: The Multi-environment Symbolic Interactive Language Grounding Benchmark 20 Oct 2021 identical code first harvested elsewhere 41c8773f66a31bbf ran · our draft was wrong fingerprinted licence of this copy not recorded
The NetHack Learning Environment 24 Jun 2020 facebookresearch/nle/nle/agent/agent.py 3fba94f2f8bc0565 unverified licence not identified · pointer only
Learning with AMIGo: Adversarially Motivated Intrinsic Goals 22 Jun 2020 facebookresearch/adversarially-motivated-intrinsic-goals/monobeast/minigrid/monobeast_amigo.py 41c8773f66a31bbf ran · our draft was wrong fingerprinted licence not identified · pointer only
RTFM: Generalising to Novel Environment Dynamics via Reading 18 Oct 2019 identical code first harvested elsewhere 41c8773f66a31bbf ran · our draft was wrong fingerprinted licence of this copy not recorded
TorchBeast: A PyTorch Platform for Distributed RL 8 Oct 2019 identical code first harvested elsewhere a27a9dc97eefda7f ran · our draft was wrong fingerprinted licence of this copy not recorded
TorchBeast: A PyTorch Platform for Distributed RL 8 Oct 2019 heiner/scalable_agent/experiment.py 50c20c55f1b07c8e unverified Apache-2.0 (permissive)
The StreetLearn Environment and Dataset 4 Mar 2019 deepmind/streetlearn/streetlearn/python/experiment.py c2a99f60b418a579 unverified Apache-2.0 (permissive)
Multi-task Deep Reinforcement Learning with PopArt 12 Sep 2018 identical code first harvested elsewhere a27a9dc97eefda7f ran · our draft was wrong fingerprinted licence of this copy not recorded

This site shows no code text; each File cell links to the file on GitHub at the repository's current default branch, which may have changed since the 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 cell for the reason. Per-sample records for a paper are on its paper page under "Code Syntology ran".

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