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The dataset generation failed
Error code: DatasetGenerationError
Exception: ValueError
Message: Expected object or value
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1816, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 281, in _generate_tables
examples = [ujson_loads(line) for line in batch.splitlines()]
~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
ValueError: Expected object or value
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
content string | domain string | fetched_at timestamp[s] | id string | license string | metadata unknown | quality float64 | source string | source_id string | title string | url string |
|---|---|---|---|---|---|---|---|---|---|---|
Critique of Agent Model
What is an agent? What constitutes agency? With the rise of Large Language Model (LLM) systems marketed as ``coding agents'', ``AI co-scientists'', and other ``agentic" tools that promise to drive up productivity, and at the same time, ``existential" concerns such as AI escaping human control w... | llm-agents | 2026-06-25T01:51:40 | 0036d81d7b2c029ebf4da4432de532a2 | arxiv-metadata | {
"authors": [
"Eric Xing",
"Mingkai Deng",
"Jinyu Hou"
],
"published": "2026-06-22T00:00:00.000Z",
"upvotes": 11
} | 0.94 | papers | 2606.23991 | Critique of Agent Model | https://huggingface.co/papers/2606.23991 |
JuliaLang/julia
The Julia Programming Language
<a name="logo"/>
<div align="center">
<a href="https://julialang.org/" target="_blank">
<img src="doc/src/assets/logo.svg" alt="Julia Logo" width="210" height="142"></img>
</a>
</div>
<table>
<!-- Docs -->
<tr>
<td>Documentation</td>
<td>
... | llm-agents | 2026-06-12T10:43:22 | 00aeb2cb671ed7fc71b8c9c123840def | mit | {
"language": "Julia",
"pushed_at": "2026-06-12T10:36:55Z",
"stars": 48818,
"topics": [
"hacktoberfest",
"hpc",
"julia",
"julia-language",
"julialang",
"machine-learning",
"numerical",
"programming-language",
"science",
"scientific"
]
} | 0.94 | github | JuliaLang/julia | JuliaLang/julia | https://github.com/JuliaLang/julia |
GROW$^2$: Grounding Which and Where for Robot Tool Use
Can the robot use a plate to cut a cake if no knife is available? Tool use greatly expands robot capabilities, but to use tools creatively beyond their intended functions, the robot faces the challenge of $\textit{open-world affordance grounding}$: select an open-... | llm-agents | 2026-06-30T05:18:38 | 00f8df128d6ba49cd28a930a66059240 | arxiv-metadata | {
"authors": [
"Yuhong Deng",
"Yuyao Liu",
"David Hsu"
],
"categories": [
"cs.RO",
"cs.AI",
"cs.CV"
],
"published": "2026-06-29T17:56:53Z"
} | 0.9031 | arxiv | 2606.30632v1 | GROW$^2$: Grounding Which and Where for Robot Tool Use | http://arxiv.org/abs/2606.30632v1 |
Characterizing Narrative Content in Web-scale LLM Pretraining Data
The narrative composition of web-scale LLM pretraining corpora remains largely unexplored even though narrative is a fundamental mode of human communication. We present the first fine-grained study of narrative features in Dolma, a 3-trillion-token ope... | llm-agents | 2026-06-22T21:11:07 | 0134e1b6765dffd939943cacbd698a4a | arxiv-metadata | {
"authors": [
"Teagan Johnson",
"Elliott Ash",
"Andrew Piper",
"Maria Antoniak"
],
"published": "2026-06-17T00:00:00.000Z",
"upvotes": 2
} | 0.9097 | papers | 2606.19468 | Characterizing Narrative Content in Web-scale LLM Pretraining Data | https://huggingface.co/papers/2606.19468 |
SkillCoach: Self-Evolving Rubrics for Evaluating and Enhancing Agentic Skill-Use
Skills are becoming a reusable operational layer for LLM agents, encoding SOPs, domain rules, tool workflows, scripts, and validation routines. In realistic skill repositories, overlapping skills make reliable skill-use difficult. Final v... | llm-agents | 2026-07-03T07:00:58 | 01e16acdff0f8a3497f514013455496f | arxiv-metadata | {
"authors": [
"Jiayin Zhu",
"Kelong Mao",
"Yudong Guo",
"Dengbo He",
"Sulong Xu",
"Simiu Gu",
"Yutao Yue"
],
"published": "2026-07-02T00:00:00.000Z",
"upvotes": 4
} | 0.9049 | papers | 2607.01874 | SkillCoach: Self-Evolving Rubrics for Evaluating and Enhancing Agentic Skill-Use | https://huggingface.co/papers/2607.01874 |
Model Forensics: Investigating Whether Concerning Behavior Reflects Misalignment
A central goal of safety research is determining whether a model is misaligned. Prior work has largely focused on detecting concerning behavior. But behavior alone does not establish misalignment: a concerning action can arise from benign... | llm-agents | 2026-06-25T05:32:13 | 02223b3a052cba0fbb617417a1de3641 | arxiv-metadata | {
"authors": [
"Aditya Singh",
"Gerson Kroiz",
"Senthooran Rajamanoharan",
"Neel Nanda"
],
"categories": [
"cs.LG",
"cs.AI"
],
"published": "2026-06-24T17:45:47Z"
} | 0.94 | arxiv | 2606.26071v1 | Model Forensics: Investigating Whether Concerning Behavior Reflects Misalignment | http://arxiv.org/abs/2606.26071v1 |
simonlesaumon/diffusiongemma-humanizer
# DiffusionGemma Humanizer
**DiffusionGemma 26B** (MoE, 3.8B active) evaluated for AI text humanization.
Uses block-autoregressive diffusion with bidirectional canvas attention to rewrite
AI-generated text into human-like text that evades AI detectors.
## Key Finding
**Diffusi... | llm-agents | 2026-06-29T22:41:07 | 02d63e9d4bf2a10f1d326bad89203b6c | apache-2.0 | {
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"kind": "model",
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"tags": [
"diffusion",
"text-humanization",
"ai-detection-evasion",
"diffusion-gemma",
"block-diffusion",
"text-generation",
"en",
"base_model:google/diffusiongemma-26B-A4B-it",
"base_model:finetune:google/diffusiongemma-26B-A4B-... | 1 | huggingface | model:simonlesaumon/diffusiongemma-humanizer | simonlesaumon/diffusiongemma-humanizer | https://huggingface.co/simonlesaumon/diffusiongemma-humanizer |
The-JDdev/GLM-5.2
# 🚀 GLM-5.2: The Ultimate 1M Context Flagship Model[cite: 1]
<div align="center">
<img src=https://raw.githubusercontent.com/zai-org/GLM-5/refs/heads/main/resources/logo.svg width="15%"/>
</div>
<p align="center">
👋 Join our <a href="https://raw.githubusercontent.com/zai-org/GLM-5/refs/heads/m... | llm-agents | 2026-06-20T20:32:41 | 02e2f41c2c5aa91918b42e06153873f8 | mit | {
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"kind": "model",
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"transformers",
"safetensors",
"glm_moe_dsa",
"text-generation",
"conversational",
"en",
"zh",
"arxiv:2602.15763",
"license:mit",
"endpoints_compatible",
"region:us"
]
} | 0.9995 | huggingface | model:The-JDdev/GLM-5.2 | The-JDdev/GLM-5.2 | https://huggingface.co/The-JDdev/GLM-5.2 |
When Behavioral Safety Evaluation Fails: A Representation-Level Perspective
Large Language Model (LLM) safety has often been evaluated at the behavior level, which provides limited evidence of internal robustness, as these evaluations target outputs rather than representation-level vulnerability under intervention. We... | llm-agents | 2026-06-11T03:32:22 | 02edcc70dc7f400a60c061346e2441ca | arxiv-metadata | {
"authors": [
"Enyi Jiang",
"Anders Gjølbye",
"Yibo Jacky Zhang",
"Sanmi Koyejo"
],
"published": "2026-06-06T00:00:00.000Z",
"upvotes": 1
} | 0.94 | papers | 2606.08044 | When Behavioral Safety Evaluation Fails: A Representation-Level Perspective | https://huggingface.co/papers/2606.08044 |
Bridging VideoQA and Video-Guided Agentic Tasks via Generalized Keyframe Extraction
Video understanding is a fundamental capability for multimodal intelligence, and recent Multimodal Large Language Models (MLLMs) have achieved remarkable performance on Video Question Answering (VideoQA) benchmarks. However, existing b... | llm-agents | 2026-06-30T05:18:46 | 0316f5dae667c977738eda214503fc22 | arxiv-metadata | {
"authors": [
"Sunqi Fan",
"Qingle Liu",
"Runqi Yin",
"Meng-Hao Guo",
"Shuojin Yang"
],
"published": "2026-06-28T00:00:00.000Z",
"upvotes": 11
} | 0.94 | papers | 2606.29445 | Bridging VideoQA and Video-Guided Agentic Tasks via Generalized Keyframe Extraction | https://huggingface.co/papers/2606.29445 |
Text-Vision Co-Instructed Image Editing
Existing image editing methods can be generally categorized into textual instruction-based and visual prompt-based ones. Textual instructions are semantically expressive, but are limited by the coarse granularity of spatial control of the editing results. In contrast, visual pro... | llm-agents | 2026-06-17T21:57:28 | 04054158e6e97ab114ed716477d4637a | arxiv-metadata | {
"authors": [
"Chenxi Xie",
"Yuhui Wu",
"Qiaosi Yi",
"Lei Zhang"
],
"published": "2026-06-15T00:00:00.000Z",
"upvotes": 11
} | 0.94 | papers | 2606.16767 | Text-Vision Co-Instructed Image Editing | https://huggingface.co/papers/2606.16767 |
Implicit Reasoning for Large Language Model-based Generative Recommendation
Large Language Models (LLMs) are increasingly adopted as backbones for Generative Recommendation (GR), promising access to pretrained world knowledge. Yet reliably invoking this knowledge for GR remains poorly understood. A key obstacle is tha... | llm-agents | 2026-06-16T15:47:36 | 040f0b8b2d661e2a9ecc6d82b7637d3b | arxiv-metadata | {
"authors": [
"Yinhan He",
"Liam Collins",
"Bhuvesh Kumar",
"Jundong Li",
"Neil Shah",
"Donald Loveland"
],
"published": "2026-06-15T00:00:00.000Z",
"upvotes": 0
} | 0.94 | papers | 2606.14142 | Implicit Reasoning for Large Language Model-based Generative Recommendation | https://huggingface.co/papers/2606.14142 |
To Run or Not to Run: Analyzing the Cost-Effectiveness of Code Execution in LLM-Based Program Repair
LLM-based agents for program repair are increasingly built on a "generate-run-revise" paradigm, iteratively executing tests to evaluate and refine patches. This execution-based approach has become standard practice in ... | llm-agents | 2026-06-29T18:05:28 | 04283261abc4ab20484a64f94b5c5bdd | arxiv-metadata | {
"authors": [
"Zhihao Lin",
"Junhua Zhu",
"Mingyi Zhou",
"Xin Wang",
"Zhensu Sun",
"Renyu Yang",
"David Lo",
"Li Li"
],
"published": "2026-06-25T00:00:00.000Z",
"upvotes": 1
} | 0.94 | papers | 2606.26978 | To Run or Not to Run: Analyzing the Cost-Effectiveness of Code Execution in LLM-Based Program Repair | https://huggingface.co/papers/2606.26978 |
blackhole33/bk-llm
# Uploaded finetuned model
- **Developed by:** blackhole33
- **License:** apache-2.0
- **Finetuned from model :** blackhole33/bk-llm
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent... | llm-agents | 2026-06-13T12:29:43 | 04490fe2970dee3a214de53e3af3951c | apache-2.0 | {
"downloads": 0,
"kind": "model",
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"tags": [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:blackhole33/bk-llm",
"base_model:finetune:blackhole33/bk-llm",
"license:apac... | 0.7282 | huggingface | model:blackhole33/bk-llm | blackhole33/bk-llm | https://huggingface.co/blackhole33/bk-llm |
Interpretation-Oriented Cloud Removal via Observation-Anchored Residual Flow with Geo-Contextual Alignment
Cloud removal (CR) is essential for optical remote sensing, serving as a prerequisite for reliable downstream interpretation, such as semantic segmentation and change detection. However, existing CR approaches of... | llm-agents | 2026-07-03T07:00:51 | 050e03fbac90b5a73ed0e120d6c3c4d5 | arxiv-metadata | {
"authors": [
"Ziyao Wang",
"Maonan Wang",
"Yucheng He",
"Xianping Ma",
"Ziyi Wang",
"Hongyang Zhang",
"Yirong Cheng",
"Man-on Pun"
],
"categories": [
"cs.CV"
],
"published": "2026-07-02T17:39:23Z"
} | 0.94 | arxiv | 2607.02471v1 | Interpretation-Oriented Cloud Removal via Observation-Anchored Residual Flow with Geo-Contextual Alignment | http://arxiv.org/abs/2607.02471v1 |
When is Your LLM Steerable?
Activation steering offers a lightweight approach to control language models' behavior at inference time, but whether it succeeds or fails heavily depends on the prompt, concept, model, and steering configuration. Finding the regime and boundaries of successful steering typically requires e... | llm-agents | 2026-06-15T02:19:24 | 068c635b5a1ebb8e134c709f8774e543 | arxiv-metadata | {
"authors": [
"Chenrui Fan",
"Yize Cheng",
"Ming Li",
"Soheil Feizi",
"Tianyi Zhou"
],
"published": "2026-06-10T00:00:00.000Z",
"upvotes": 3
} | 0.94 | papers | 2606.11599 | When is Your LLM Steerable? | https://huggingface.co/papers/2606.11599 |
ombharatiya/ai-system-design-guide
AI system design guide for engineers building production AI systems and evals.
# 🧠 AI System Design Guide
### The Complete Interview & Production Reference
<p align="center">
<a href="https://www.aidaddy.tech"><img src="https://img.shields.io/badge/Read%20it%20online%20%E2%86%92... | llm-agents | 2026-06-21T08:53:22 | 06b05346502666b3ad7da6baa948eca8 | mit | {
"language": null,
"pushed_at": "2026-06-21T08:43:44Z",
"stars": 1834,
"topics": [
"agentic-ai",
"agentic-workflow",
"ai",
"ai-jobs",
"artificial-intelligence",
"aws",
"azure",
"claude",
"evals",
"forward-deployed-engineer"
]
} | 0.9999 | github | ombharatiya/ai-system-design-guide | ombharatiya/ai-system-design-guide | https://github.com/ombharatiya/ai-system-design-guide |
apphp/awesome-php-ml
The most comprehensive curated list of Machine Learning, Artificial Intelligence, NLP, LLM, and Data Science libraries for PHP
# Awesome PHP Machine Learning & AI
[](https://awesome.re)
[![GitHub stars](https://img.shields.io/github/stars/apphp/awesome-php... | llm-agents | 2026-06-25T14:14:33 | 06cc43d3eec03312f57248a62dcf4a9f | mit | {
"language": "PHP",
"pushed_at": "2026-06-25T14:04:02Z",
"stars": 115,
"topics": [
"ai",
"awesome-list",
"llm",
"machine-learning",
"ml",
"nlp",
"php",
"php-ai",
"php-ml"
]
} | 1 | github | apphp/awesome-php-ml | apphp/awesome-php-ml | https://github.com/apphp/awesome-php-ml |
VenkataRamanaKurumallajaddangi/Telugu7200Best
# 🤖 Jaddangi AI Telugu 500M (V7200)
**Jaddangi AI Telugu 78M Instruct** is a custom-built, 78-million parameter Transformer model designed specifically for the Telugu language. Built entirely from scratch, this model features advanced architectural optimizations like Gr... | llm-agents | 2026-07-01T10:49:08 | 06d1ff7f2a6e0d6c0f6808bf51536624 | apache-2.0 | {
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"te",
"base_model:VenkataRamanaKurumallajaddangi/Telugu",
"base_model:finetune:VenkataRamanaKurumallajaddangi/Telugu",
"license:apache-2.0",
"region:us"
]
} | 0.998 | huggingface | model:VenkataRamanaKurumallajaddangi/Telugu7200Best | VenkataRamanaKurumallajaddangi/Telugu7200Best | https://huggingface.co/VenkataRamanaKurumallajaddangi/Telugu7200Best |
Trade-offs in Medical LLM Adaptation: An Empirical Study in French QA
The development of large language models (LLMs) has led to an increased focus on their adaptation to specialized domains and languages, yet the effectiveness of domain adaptation strategies remains unclear. We present a study of medical domain adapt... | llm-agents | 2026-06-18T03:16:22 | 0705bd18f01a46b2b90a1d9eec2f9b69 | arxiv-metadata | {
"authors": [
"Ikram Belmadani",
"Oumaima El Khettari",
"Carlos Ramisch",
"Frederic Bechet",
"Richard Dufour",
"Benoit Favre"
],
"categories": [
"cs.CL",
"cs.AI"
],
"published": "2026-06-17T16:42:22Z"
} | 0.9097 | arxiv | 2606.19266v1 | Trade-offs in Medical LLM Adaptation: An Empirical Study in French QA | http://arxiv.org/abs/2606.19266v1 |
A Verifiable Search Is Not a Learnable Chain-of-Thought
It is tempting to assume any task solvable by a short program can be taught to a model as its chain-of-thought: write the steps out, fine-tune, and the model follows. This paper shows the assumption fails for an identifiable class of procedures. The testbed is ni... | llm-agents | 2026-06-23T18:19:26 | 0733099ba480684731358e5cb4a5cb02 | arxiv-metadata | {
"authors": [
"Harsh Patel"
],
"published": "2026-06-20T00:00:00.000Z",
"upvotes": 1
} | 0.94 | papers | 2606.21884 | A Verifiable Search Is Not a Learnable Chain-of-Thought | https://huggingface.co/papers/2606.21884 |
On the Limits of LLM Adaptability: Impact of Model-Internalized Priors on Annotation Task Performance
Large Language Models (LLMs) are increasingly used for zero-shot annotation and LLM-as-a-judge tasks, yet their reliability hinges on how model-internalized priors interact with user-provided instructions. We investig... | llm-agents | 2026-06-12T21:31:17 | 0820327d21f6505da5d44de026de4fe1 | arxiv-metadata | {
"authors": [
"Etienne Casanova",
"Rafal Kocielnik",
"R. Michael Alvarez"
],
"published": "2026-05-30T00:00:00.000Z",
"upvotes": 0
} | 0.94 | papers | 2606.00467 | On the Limits of LLM Adaptability: Impact of Model-Internalized Priors on Annotation Task Performance | https://huggingface.co/papers/2606.00467 |
groxaxo/Qwen3.5-24.5B-Reapped-v1
# Qwen3.5-24.5B-Reapped-v1
**A leaner, coding-sharpened Qwen3.5 MoE.** This model takes a 35B-class Qwen3.5 Mixture-of-Experts,
**REAPs away ~30% of its experts** to land at **~24.5B total parameters (≈3B active per token)**, then
**bakes in a coding/agentic LoRA** so the slimmer netw... | llm-agents | 2026-06-26T05:19:24 | 08ba5413143b95f20cc4c8c3016e11c9 | apache-2.0 | {
"downloads": 0,
"kind": "model",
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"transformers",
"safetensors",
"qwen3_5_moe_text",
"text-generation",
"qwen3_5_moe",
"moe",
"reap",
"pruned",
"coding",
"agentic",
"lora-merged",
"qlora",
"conversational",
"en",
"license:apache-... | 0.9994 | huggingface | model:groxaxo/Qwen3.5-24.5B-Reapped-v1 | groxaxo/Qwen3.5-24.5B-Reapped-v1 | https://huggingface.co/groxaxo/Qwen3.5-24.5B-Reapped-v1 |
Accuracy and Satisfaction in Multi-Turn LLM Dialogues for NFR Assessment
LLM-based dialogue assistants have become mainstream tools for software developers, yet current evaluation benchmarks focus exclusively on functional correctness. This leaves a critical gap in assessing the quality and accuracy of these conversat... | llm-agents | 2026-06-24T21:49:09 | 0937fb3794796d9b16f36425f6c83910 | arxiv-metadata | {
"authors": [
"Ali Pourghasemi Fatideh",
"Wilder Baldwin",
"Maria Dhakal",
"Collin McMillan",
"Sepideh Ghanavati"
],
"categories": [
"cs.AI"
],
"published": "2026-06-23T17:15:40Z"
} | 0.94 | arxiv | 2606.24834v1 | Accuracy and Satisfaction in Multi-Turn LLM Dialogues for NFR Assessment | http://arxiv.org/abs/2606.24834v1 |
Look Light, Think Heavy: What Multimodal Chain-of-Thought Reasoning Can and Cannot Do
Chain-of-Thought (CoT) has become a standard method for improving reasoning capabilities in large language models (LLMs) by eliciting step-by-step thinking, but its effectiveness in multimodal tasks remains unclear. In this paper, we... | llm-agents | 2026-06-25T03:40:59 | 0938fac730d75d1113f27169e67a281f | arxiv-metadata | {
"authors": [
"Zhuoran Jin",
"Kejian Zhu",
"Hongbang Yuan",
"Yupu Hao",
"Pengfei Cao",
"Yubo Chen",
"Kang Liu",
"Jun Zhao"
],
"published": "2026-06-21T00:00:00.000Z",
"upvotes": 4
} | 0.94 | papers | 2606.22565 | Look Light, Think Heavy: What Multimodal Chain-of-Thought Reasoning Can and Cannot Do | https://huggingface.co/papers/2606.22565 |
nur-dev/farabi-4b
transformers safetensors qwen3 text-generation kazakh russian rag tool-calling agent conversational kk ru en license:apache-2.0 text-generation-inference | llm-agents | 2026-07-05T13:48:08 | 0979fbe761a954d182cc5860f642f5ab | apache-2.0 | {
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"agent",
"conversational",
"kk",
"ru",
"en",
"license:apache-2.0",
"text-generation-inferenc... | 0.6745 | huggingface | model:nur-dev/farabi-4b | nur-dev/farabi-4b | https://huggingface.co/nur-dev/farabi-4b |
InternScience/Agents-A1-FP8
# Agents-A1: Scaling the Horizon, Not the Parameters: Reaching Trillion-Parameter Performance with a 35B Agent
<div style="display: flex; flex-direction: column; align-items: center; line-height: 1.2;">
<div style="display: flex; justify-content: center; align-items: center; gap: 10px; h... | llm-agents | 2026-07-03T07:00:54 | 09957d3dc6b4c9a7244de9e452a98047 | apache-2.0 | {
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"transformers",
"safetensors",
"qwen3_5_moe",
"image-text-to-text",
"quantized",
"moe",
"mixture-of-experts",
"vlm",
"vision",
"agentic",
"text-generation",
"conversational",
"arxiv:2606.30616",
"bas... | 0.9397 | huggingface | model:InternScience/Agents-A1-FP8 | InternScience/Agents-A1-FP8 | https://huggingface.co/InternScience/Agents-A1-FP8 |
AEON-7/Ornith-1.0-35B-AEON-Ultimate-Uncensored-FP8
# Ornith-1.0-35B-AEON-Ultimate-Uncensored-FP8
**compressed-tensors FP8** (per-channel weight + per-token dynamic activation) build of the uncensored [Ornith-1.0-35B AEON Ultimate](https://huggingface.co/AEON-7/Ornith-1.0-35B-AEON-Ultimate-Uncensored-BF16) — for effic... | llm-agents | 2026-06-27T14:39:09 | 09db90909dc169cf9c888b8582ec8860 | mit | {
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"transformers",
"safetensors",
"qwen3_5_moe",
"image-text-to-text",
"abliterated",
"uncensored",
"refusal-removed",
"abliterix",
"aeon",
"aeon-7",
"gated-deltanet",
"moe",
"reasoning",
"thinking",
... | 0.9943 | huggingface | model:AEON-7/Ornith-1.0-35B-AEON-Ultimate-Uncensored-FP8 | AEON-7/Ornith-1.0-35B-AEON-Ultimate-Uncensored-FP8 | https://huggingface.co/AEON-7/Ornith-1.0-35B-AEON-Ultimate-Uncensored-FP8 |
RepoRescue: An Empirical Study of LLM Agents on Whole-Repository Compatibility Rescue
Open-source libraries and tools are widely reused, but compatibility maintenance is expensive. Once maintainers leave, useful repositories can stop working as runtimes and dependencies evolve. We study whether LLM agents can adapt ol... | llm-agents | 2026-07-02T03:22:54 | 0a082c0fed330189740307ef3a4e76f3 | arxiv-metadata | {
"authors": [
"Zhihao Lin",
"Mingyi Zhou",
"Zhensu Sun",
"Yizhuo Yang",
"Renyu Yang",
"David Lo",
"Li Li"
],
"published": "2026-07-01T00:00:00.000Z",
"upvotes": 1
} | 0.94 | papers | 2607.01213 | RepoRescue: An Empirical Study of LLM Agents on Whole-Repository Compatibility Rescue | https://huggingface.co/papers/2607.01213 |
Autodata: An agentic data scientist to create high quality synthetic data
We introduce Autodata, a general method that enables AI agents to act as data scientists who build high quality training and evaluation data. We show how to train (meta-optimize) such a data scientist agent, so that it learns to create even stro... | llm-agents | 2026-06-25T03:40:59 | 0a1476ba8ba7cf9ac6e181d4932e8869 | arxiv-metadata | {
"authors": [
"Ilia Kulikov",
"Chenxi Whitehouse",
"Tianhao Wu",
"Yixin Nie",
"Swarnadeep Saha",
"Eryk Helenowski",
"Weizhe Yuan",
"Olga Golovneva"
],
"published": "2026-06-24T00:00:00.000Z",
"upvotes": 3
} | 0.8293 | papers | 2606.25996 | Autodata: An agentic data scientist to create high quality synthetic data | https://huggingface.co/papers/2606.25996 |
SproutRAG: Attention-Guided Tree Search with Progressive Embeddings for Long-Document RAG
Retrieval-augmented generation (RAG) systems must balance retrieval granularity with contextual coherence, a challenge that existing methods address through LLM-guided chunking, single-level context expansion, or hierarchical sum... | llm-agents | 2026-06-22T21:11:07 | 0a7953b83e575c229dc73f3eec183ff4 | arxiv-metadata | {
"authors": [
"Amirhossein Abaskohi",
"Issam H. Laradji",
"Peter West",
"Giuseppe Carenini"
],
"published": "2026-06-16T18:28:00.000Z",
"upvotes": 6
} | 0.94 | papers | 2606.18381 | SproutRAG: Attention-Guided Tree Search with Progressive Embeddings for Long-Document RAG | https://huggingface.co/papers/2606.18381 |
Deeper is Not Always Better: Mitigating the Alignment Tax via Confident Layer Decoding
Autoregressive generation in large language models (LLMs) conventionally decodes from the final layer, assuming that deeper representations yield more reliable next-token predictions. We revisit this assumption by revealing a recurr... | llm-agents | 2026-06-23T07:39:24 | 0a89bd84f6c6e297a09fd81c367a47d3 | arxiv-metadata | {
"authors": [
"Xuanming Zhang",
"Sining Zhoubian",
"Yuxuan Chen",
"Tianyi Tang",
"An Yang",
"Sean Du",
"Chujie Zheng",
"Fei Huang"
],
"published": "2026-06-20T00:00:00.000Z",
"upvotes": 3
} | 0.9133 | papers | 2606.21906 | Deeper is Not Always Better: Mitigating the Alignment Tax via Confident Layer Decoding | https://huggingface.co/papers/2606.21906 |
ReContext: Recursive Evidence Replay as LLM Harness for Long-Context Reasoning
Understanding and reasoning over long contexts has become a key requirement for deploying large language models (LLMs) in realistic applications. Although recent LLMs support increasingly long context windows, they often fail to use relevan... | llm-agents | 2026-07-03T07:00:51 | 0a8a81d930410d1b67a147696c282cea | arxiv-metadata | {
"authors": [
"Yanjun Zhao",
"Ruizhong Qiu",
"Tianxin Wei",
"Yuanchen Bei",
"Zhining Liu",
"Lingjie Chen",
"Ismini Lourentzou",
"Hanghang Tong"
],
"categories": [
"cs.AI"
],
"published": "2026-07-02T17:59:26Z"
} | 0.9307 | arxiv | 2607.02509v1 | ReContext: Recursive Evidence Replay as LLM Harness for Long-Context Reasoning | http://arxiv.org/abs/2607.02509v1 |
spare-rl/qwen3-30b-a3b-0703-fixed-rlve-official16-iter223
# Qwen3-30B-A3B-Instruct-2507
<a href="https://chat.qwen.ai/?model=Qwen3-30B-A3B-2507" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; verti... | llm-agents | 2026-07-06T17:06:38 | 0ad06168b9bddc6f7c8578050111bbe4 | apache-2.0 | {
"downloads": 0,
"kind": "model",
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"tags": [
"transformers",
"safetensors",
"qwen3_moe",
"text-generation",
"conversational",
"arxiv:2402.17463",
"arxiv:2407.02490",
"arxiv:2501.15383",
"arxiv:2404.06654",
"arxiv:2505.09388",
"license:apache-2.0",
"end... | 1 | huggingface | model:spare-rl/qwen3-30b-a3b-0703-fixed-rlve-official16-iter223 | spare-rl/qwen3-30b-a3b-0703-fixed-rlve-official16-iter223 | https://huggingface.co/spare-rl/qwen3-30b-a3b-0703-fixed-rlve-official16-iter223 |
AC-ODM: Actor--Critic Online Data Mixing for Sample-Efficient LLM Pretraining
Optimizing pretraining data composition is pivotal for LLM generalization. While dynamic mixing outperforms static strategies by capturing evolving training dynamics, current methods fail to reconcile computational efficiency with sample eff... | llm-agents | 2026-06-23T15:03:24 | 0b6b1c10b17f3b2c240c1cde63d3d057 | arxiv-metadata | {
"authors": [
"Jing Ma",
"Chenhao Dang",
"Mingjie Liao"
],
"published": "2026-06-14T00:00:00.000Z",
"upvotes": 1
} | 0.9325 | papers | 2505.23878 | AC-ODM: Actor--Critic Online Data Mixing for Sample-Efficient LLM Pretraining | https://huggingface.co/papers/2505.23878 |
Gryphe/Gemma-4-31B-StyleTune
# Gemma-4-31B-StyleTune
[](Gemma-4-31B-StyleTune.jpg)
A happy accident in surgical finetuning - 60% fewer clichés, an entirely new writing style, and the same Gemma 4 31B you already know underneath. One tensor changed out of 834.
## What is a styl... | llm-agents | 2026-06-12T19:50:28 | 0d18f8abb6bedb95ae0072591726b8b5 | apache-2.0 | {
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"safetensors",
"gemma4",
"conversational",
"instruct",
"finetune",
"roleplay",
"creative-writing",
"style-tune",
"text-generation",
"en",
"base_model:google/gemma-4-31B-it",
"base_model:finetune:google/gemm... | 0.9397 | huggingface | model:Gryphe/Gemma-4-31B-StyleTune | Gryphe/Gemma-4-31B-StyleTune | https://huggingface.co/Gryphe/Gemma-4-31B-StyleTune |
Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application
Environments serve as interactive systems for large language model (LLM) based agents across diverse scenarios and play a crucial role in driving the continual evolution of model capabili... | llm-agents | 2026-06-11T03:30:13 | 0d9456fc4c98ec57a9fcb3f944e3abdf | arxiv-metadata | {
"authors": [
"Jiachun Li",
"Zhuoran Jin",
"Tianyi Men",
"Yupu Hao",
"Kejian Zhu",
"Lingshuai Wang",
"Dongqi Huang",
"Longxiang Wang"
],
"published": "2026-06-10T00:00:00.000Z",
"upvotes": 20
} | 0.94 | papers | 2606.12191 | Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application | https://huggingface.co/papers/2606.12191 |
Diffusion-GR2: Diffusion Generative Reasoning Re-ranker
Generative reasoning re-rankers achieve strong recommendation accuracy by emitting a chain-of-thought before re-ordering a candidate list, but they are slow at inference: an autoregressive (AR) decoder spends one sequential forward pass per reasoning token, and t... | llm-agents | 2026-07-02T06:08:11 | 0dc585faeab2ae2eec8c8febd74db557 | arxiv-metadata | {
"authors": [
"Zhuoxuan Zhang",
"Kangqi Ni",
"Yuhang Chen",
"Mingfu Liang",
"Xiaohan Wei",
"Yunchen Pu",
"Fei Tian",
"Chonglin Sun"
],
"categories": [
"cs.IR",
"cs.AI"
],
"published": "2026-07-01T17:02:20Z"
} | 0.94 | arxiv | 2607.01170v1 | Diffusion-GR2: Diffusion Generative Reasoning Re-ranker | http://arxiv.org/abs/2607.01170v1 |
RSF-GLLM: Bridging the Semantic Gap in Multi-Hop Knowledge Graph QA via Recurrent Soft-Flow and Decoupled LLM Generation
Multi-hop Question Answering over Knowledge Graphs faces a critical challenge: traditional retrieve-then-read pipelines break differentiability, preventing the retriever from learning to bridge the ... | llm-agents | 2026-07-08T06:31:47 | 0ecc8571ab0675fdfe6159a21e927d7c | arxiv-metadata | {
"authors": [
"Sambaran Bandyopadhyay",
"Ananth Muppidi"
],
"categories": [
"cs.CL",
"cs.AI"
],
"published": "2026-07-07T17:32:36Z"
} | 0.8773 | arxiv | 2607.06527v1 | RSF-GLLM: Bridging the Semantic Gap in Multi-Hop Knowledge Graph QA via Recurrent Soft-Flow and Decoupled LLM Generation | http://arxiv.org/abs/2607.06527v1 |
unsloth/Qwen-AgentWorld-35B-A3B-GGUF
<div>
<p style="margin-top: 0;margin-bottom: 0;">
<em><a href="https://docs.unsloth.ai/basics/unsloth-dynamic-v2.0-gguf">Unsloth Dynamic 2.0</a> achieves superior accuracy & outperforms other leading quants.</em>
</p>
<div style="display: flex; gap: 5px; align-items: center... | llm-agents | 2026-06-25T01:51:38 | 0ed1b4dd4500951a10b31a3e11fab15b | apache-2.0 | {
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"gguf",
"qwen",
"unsloth",
"world-model",
"agent",
"environment-simulation",
"text-generation",
"dataset:Qwen/AgentWorldBench",
"arxiv:2606.24597",
"base_model:Qwen/Qwen-AgentWorld-35B-A3B",
... | 0.9995 | huggingface | model:unsloth/Qwen-AgentWorld-35B-A3B-GGUF | unsloth/Qwen-AgentWorld-35B-A3B-GGUF | https://huggingface.co/unsloth/Qwen-AgentWorld-35B-A3B-GGUF |
MRockatansky/gemma-4-31B-anthology
<div align="center">
<h1>📚 Anthology 31B</h1>
<p><b>A Gemma-4-31B merge focused on creative storywriting.</b></p>
<!-- This is the placeholder for your attached repository image -->
<img src="./image.png" alt="Anthology 31B Header Image" width="700px" style="border-radius... | llm-agents | 2026-07-03T15:24:11 | 0f1703da9e7564d3b0d8ddfacfd7a811 | apache-2.0 | {
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"tags": [
"safetensors",
"gemma4",
"merge",
"mergekit",
"creative-writing",
"storytelling",
"text-generation",
"base_model:MRockatansky/Gemma-4-31B-storymaxxed2",
"base_model:merge:MRockatansky/Gemma-4-31B-storymaxxed2",
"bas... | 0.9995 | huggingface | model:MRockatansky/gemma-4-31B-anthology | MRockatansky/gemma-4-31B-anthology | https://huggingface.co/MRockatansky/gemma-4-31B-anthology |
MuSViT: A Foundation Vision Model for Sheet Music Representation
Foundation models have transformed vision and language processing by providing rich, reusable representations that transfer across diverse tasks. Sheet music, as a visual encoding of musical language, lacks such a strong domain-specific backbone. We intr... | llm-agents | 2026-07-01T10:49:09 | 0f66cf8bcc19330cfa170d144e1a3ab6 | arxiv-metadata | {
"authors": [
"Carlos Penarrubia",
"Antonio Rios-Vila",
"Eliseo Fuentes-Martinez",
"Juan C. Martinez-Sevilla",
"Francisco J. Castellanos",
"María Alfaro-Contreras",
"Jorge Calvo-Zaragoza"
],
"published": "2026-06-30T00:00:00.000Z",
"upvotes": 1
} | 0.94 | papers | 2606.31811 | MuSViT: A Foundation Vision Model for Sheet Music Representation | https://huggingface.co/papers/2606.31811 |
dawahealth/medgemma-1.5-4b-4bit-v1
# MedGemma 1.5 4B IT (4-bit Quantized)
## Overview
This repository contains a **4-bit quantized** version of Google's **MedGemma 1.5 4B**, specifically optimized for efficient deployment on resource-constrained hardware. By leveraging `bitsandbytes` quantization, this model signific... | llm-agents | 2026-06-18T10:03:38 | 0fe84bc67450a50056ceec854817aee1 | mit | {
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"kind": "model",
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"tags": [
"safetensors",
"gemma3",
"medical",
"quantized",
"bitsandbytes",
"4bit",
"gemma",
"zambia",
"text-generation",
"conversational",
"en",
"dataset:bigbio/pubmed_qa",
"base_model:google/medgemma-1.5-4b-it",
... | 1 | huggingface | model:dawahealth/medgemma-1.5-4b-4bit-v1 | dawahealth/medgemma-1.5-4b-4bit-v1 | https://huggingface.co/dawahealth/medgemma-1.5-4b-4bit-v1 |
zaid646/tinyllama-1.1b-alpaca-qlora
# TinyLlama-1.1B Alpaca QLoRA
This model is a QLoRA (4-bit) fine-tuned adapter of `TinyLlama/TinyLlama-1.1B-Chat-v1.0` on the Alpaca instruction-following dataset.
- **Base model:** [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0)
- *... | llm-agents | 2026-07-01T13:34:37 | 1004c49d3281b08fac0ef454a51cddc1 | mit | {
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"peft",
"safetensors",
"TinyLlama",
"QLoRA",
"alpaca",
"instruction-tuning",
"lora",
"text-generation",
"conversational",
"en",
"dataset:yahma/alpaca-cleaned",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0"... | 1 | huggingface | model:zaid646/tinyllama-1.1b-alpaca-qlora | zaid646/tinyllama-1.1b-alpaca-qlora | https://huggingface.co/zaid646/tinyllama-1.1b-alpaca-qlora |
Which Models Are Our Models Built On? Auditing Invisible Dependencies in Modern LLMs
Modern LLM training pipelines increasingly rely on other models to generate data, filter corpora, judge outputs, and guide development decisions. These dependencies are recursive: a model may depend on an upstream artifact whose own d... | llm-agents | 2026-06-11T03:32:17 | 10f6cbe83ebe34040db1afcbe51d7fe6 | arxiv-metadata | {
"authors": [
"Sanjay Adhikesaven",
"Haoxiang Sun",
"Sewon Min"
],
"categories": [
"cs.CL"
],
"published": "2026-06-10T17:47:59Z"
} | 0.94 | arxiv | 2606.12385v1 | Which Models Are Our Models Built On? Auditing Invisible Dependencies in Modern LLMs | http://arxiv.org/abs/2606.12385v1 |
Dr-DCI: Scaling Direct Corpus Interaction via Dynamic Workspace Expansion
Agentic search over large corpora relies on retriever-mediated interfaces (e.g., BM25 or ColBERT) for scalable candidate discovery. While effective at ranking relevant documents, these interfaces expose evidence only as ranked results or bounded... | llm-agents | 2026-06-17T21:57:28 | 11646a4c86e1eddd6bb513b7704d2bb2 | arxiv-metadata | {
"authors": [
"Yi Lu",
"Zhuofeng Li",
"Ping Nie",
"Haoxiang Zhang",
"Yuyu Zhang",
"Kai Zou",
"Wenhu Chen",
"Jimmy Lin"
],
"published": "2026-06-12T00:00:00.000Z",
"upvotes": 7
} | 0.94 | papers | 2606.14885 | Dr-DCI: Scaling Direct Corpus Interaction via Dynamic Workspace Expansion | https://huggingface.co/papers/2606.14885 |
MemSlides: A Hierarchical Memory Driven Agent Framework for Personalized Slide Generation with Multi-turn Local Revision
Personalized presentation generation requires more than conditioning on a current prompt or template: agents must preserve stable user preferences across tasks, retain newly introduced preferences a... | llm-agents | 2026-06-22T16:28:49 | 1165029dffbd0e9a85f672e94b56ff7e | arxiv-metadata | {
"authors": [
"Ye Jin",
"Yangyang Xu",
"Jun Zhu",
"Yibo Yang"
],
"published": "2026-06-15T00:00:00.000Z",
"upvotes": 13
} | 0.94 | papers | 2606.17162 | MemSlides: A Hierarchical Memory Driven Agent Framework for Personalized Slide Generation with Multi-turn Local Revision | https://huggingface.co/papers/2606.17162 |
Multi-scale Object-Aware Gaze Estimation via Geometric Reasoning
Gaze target estimation aims to predict the semantic object an observer fixates upon within an image, a task deeply rooted in the object-oriented nature of human gaze. Observers tend to select a specific semantic entity as the attentional target, rather t... | llm-agents | 2026-06-30T01:59:40 | 11d826f3436c7dec032cd11f27e99f1f | arxiv-metadata | {
"authors": [
"Jiajie Mi",
"Xinyu Liu",
"Mengke Song",
"Chenglizhao Chen"
],
"categories": [
"cs.CV"
],
"published": "2026-06-28T11:02:21Z"
} | 0.9355 | arxiv | 2606.29334v1 | Multi-scale Object-Aware Gaze Estimation via Geometric Reasoning | http://arxiv.org/abs/2606.29334v1 |
suryatmodulus/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-GGUF
# 💻🤖 Gemma4-12B **v2** — Coding + Agentic Edition ✨
### 🐣 Tiny footprint, big brain — a local **coding & tool-using agent** for *everyone*
> **No matter your GPU. No matter your RAM.** With **~4.5 GB** of VRAM *or* unified memory free, you can ... | llm-agents | 2026-06-19T20:25:32 | 11d8ed3439d395306378ca8a80b6f0d6 | apache-2.0 | {
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"gguf",
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"coding",
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"terminal",
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"reasoning",
"thinking",
"llama.cpp",
"local-llm",
"text-generation",
"base_model:google/gemma-4-12B-it",
"base_model:quantized:google/gemma... | 0.9377 | huggingface | model:suryatmodulus/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-GGUF | suryatmodulus/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-GGUF | https://huggingface.co/suryatmodulus/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-GGUF |
Gryphe/Gemma-4-26B-A4B-StyleTune-V2
# Gemma-4-26B-A4B-StyleTune-V2
[](Gemma-4-26B-A4B-StyleTune-V2.jpg)
As promised, a slightly less wonky 26B-A4B Style Tune! Turns out you really shouldn't use this technique with multiple epochs. I delved deep into the data and found th... | llm-agents | 2026-06-20T15:20:18 | 11e7474bd11d35bdadc9ccdaeb763cc5 | apache-2.0 | {
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"safetensors",
"gemma4",
"conversational",
"instruct",
"finetune",
"roleplay",
"creative-writing",
"style-tune",
"text-generation",
"en",
"base_model:google/gemma-4-26B-A4B-it",
"base_model:finetune:google/g... | 0.94 | huggingface | model:Gryphe/Gemma-4-26B-A4B-StyleTune-V2 | Gryphe/Gemma-4-26B-A4B-StyleTune-V2 | https://huggingface.co/Gryphe/Gemma-4-26B-A4B-StyleTune-V2 |
kubeflow/trainer
Distributed AI Model Training and LLM Fine-Tuning on Kubernetes | llm-agents | 2026-06-12T12:29:08 | 12ad5614415d7a5dc986db96e10ae04a | apache-2.0 | {
"language": "Go",
"pushed_at": "2026-06-12T12:07:37Z",
"stars": 2112,
"topics": [
"ai",
"distributed",
"fine-tuning",
"gpu",
"huggingface",
"jax",
"kubeflow",
"kubernetes",
"llm",
"machine-learning"
]
} | 0.6562 | github | kubeflow/trainer | kubeflow/trainer | https://github.com/kubeflow/trainer |
gaasher/Agent-Loop-Skills
Loop until it's better — drop-in agentic loops (autoresearch, scientific writing, data analysis, code/SQL/prompt optimization, red-teaming) as open-standard Agent Skills. Verification-gated; native on Claude Code, portable across Codex, Cursor & other Skills hosts. | llm-agents | 2026-06-29T01:10:06 | 12e9c64f8a4e85c022d5328c673e4ec3 | mit | {
"language": "Python",
"pushed_at": "2026-06-29T00:50:51Z",
"stars": 113,
"topics": [
"agent-skills",
"agentic-loops",
"agentic-workflows",
"ai-agents",
"anthropic",
"autoresearch",
"claude",
"claude-code",
"data-analysis",
"literature-review"
]
} | 0.698 | github | gaasher/Agent-Loop-Skills | gaasher/Agent-Loop-Skills | https://github.com/gaasher/Agent-Loop-Skills |
Real vs. Complex Spectral Bases for Neural Operators: The Role of Green's Function Alignment
Fourier Neural Operators (FNO) learn solution operators of partial differential equations by parameterizing global convolutions in the complex Fourier domain. For real-valued PDE solutions, the complex FFT carries representati... | llm-agents | 2026-06-24T21:49:09 | 12f8845a947f82503b110c9fa1c747fa | arxiv-metadata | {
"authors": [
"Jason Sulskis",
"Sathya Ravi"
],
"categories": [
"cs.LG"
],
"published": "2026-06-23T17:29:15Z"
} | 0.94 | arxiv | 2606.24851v1 | Real vs. Complex Spectral Bases for Neural Operators: The Role of Green's Function Alignment | http://arxiv.org/abs/2606.24851v1 |
VenkataRamanaKurumallajaddangi/Telugu7200
# 🤖 Jaddangi AI Telugu 500M (V7200)
**Jaddangi AI Telugu 78M Instruct** is a custom-built, 78-million parameter Transformer model designed specifically for the Telugu language. Built entirely from scratch, this model features advanced architectural optimizations like Groupe... | llm-agents | 2026-06-21T12:11:25 | 130e13e979e69bdfa4df12da84d1dad6 | apache-2.0 | {
"downloads": 0,
"kind": "model",
"likes": 1,
"tags": [
"text-generation",
"pytorch",
"custom-architecture",
"te",
"base_model:VenkataRamanaKurumallajaddangi/Telugu",
"base_model:finetune:VenkataRamanaKurumallajaddangi/Telugu",
"license:apache-2.0",
"region:us"
]
} | 0.998 | huggingface | model:VenkataRamanaKurumallajaddangi/Telugu7200 | VenkataRamanaKurumallajaddangi/Telugu7200 | https://huggingface.co/VenkataRamanaKurumallajaddangi/Telugu7200 |
yrrhall/Qwen3-0.6B
# Qwen3-0.6B
<a href="https://chat.qwen.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Qwen3 Highlights
Qwen3 is the latest generation o... | llm-agents | 2026-07-06T13:33:10 | 132e3f16569261e127e78939b29c4380 | apache-2.0 | {
"downloads": 0,
"kind": "model",
"likes": 0,
"tags": [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:2505.09388",
"base_model:Qwen/Qwen3-0.6B-Base",
"base_model:finetune:Qwen/Qwen3-0.6B-Base",
"license:apache-2.0",
"text-generation-... | 1 | huggingface | model:yrrhall/Qwen3-0.6B | yrrhall/Qwen3-0.6B | https://huggingface.co/yrrhall/Qwen3-0.6B |
MemSyco-Bench: Benchmarking Sycophancy in Agent Memory
Memory has emerged as a cornerstone of modern LLM-based agents, supporting their evolution from single-turn assistants to long-term collaborators. However, memory is not always beneficial: retrieved memories often induce a critical issue of sycophancy, causing age... | llm-agents | 2026-07-02T06:08:18 | 132eee87dd7597bcc2c4c51c35923b37 | arxiv-metadata | {
"authors": [
"Zhishang Xiang",
"Zerui Chen",
"Yunbo Tang",
"Zhimin Wei",
"Ruqin Ning",
"Yujie Lin",
"Qinggang Zhang",
"Jinsong Su"
],
"published": "2026-07-01T00:00:00.000Z",
"upvotes": 5
} | 0.8842 | papers | 2607.01071 | MemSyco-Bench: Benchmarking Sycophancy in Agent Memory | https://huggingface.co/papers/2607.01071 |
Correct Yourself, Keep My Trust: How Self-Correction and Social Connection Shape Credibility in Social Chatbots
When social chatbots make mistakes, and they do, how they recover determines whether users trust them again. Social chatbots are increasingly integrated into everyday life, yet they remain prone to generatin... | llm-agents | 2026-06-18T03:16:22 | 1394eda55f9d4e72ceba7d24306d2621 | arxiv-metadata | {
"authors": [
"Biswadeep Sen",
"Yi-Chieh Lee"
],
"categories": [
"cs.HC",
"cs.AI",
"cs.CY"
],
"published": "2026-06-17T17:04:41Z"
} | 0.94 | arxiv | 2606.19286v1 | Correct Yourself, Keep My Trust: How Self-Correction and Social Connection Shape Credibility in Social Chatbots | http://arxiv.org/abs/2606.19286v1 |
End of preview.
llm-agents-corpus v92
Auto-built (demand): 1 open request(s) and 0 recent download(s) for 'llm-agents' with no dataset newer than 14 days
- Kind: scraped
- Domain: llm-agents
- Records: 702
- Created: 2026-07-08T17:36:14+00:00
- SHA-256:
74c00af747e66d1d4abf248168263fb9d5f1e424182d7adcdef160533c32dae2 - Pipeline: v2.0.0
- Filters:
{"min_quality": 0.55, "limit": 1000, "source": null, "backend": null, "min_judge": null}
Sources
- huggingface: 301
- papers: 197
- arxiv: 145
- github: 59
Licenses
- arxiv-metadata: 342
- apache-2.0: 246
- mit: 98
- cc-by-4.0: 13
- cc0-1.0: 3
Provenance & reproducibility
Every line in data.jsonl carries its source/provenance. manifest.json
pins the exact record ids — gene rebuild --manifest manifest.json
regenerates this dataset byte-identically (verified by SHA-256).
How this dataset was made
Built by Gene, a provenance-first training-data pipeline: sources are scraped from ArXiv, GitHub, and Hugging Face (permissive licenses only), and synthetic examples pass a six-stage gate — generation, a critique-and-revise editor pass, an LLM judge, an adversarial second judge, evidence verification (every kept pair carries a quote that provably appears in its source), and sandboxed execution for code. manifest.json pins the exact records: the dataset regenerates byte-identically (SHA-256 verified).
Custom datasets built to order — open an issue on this repo or see the profile for contact.
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