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rajkumarrawalΒ  submitted a paper 16 days ago
Learning to Act and Cooperate for Distributed Black-Box Consensus Optimization
rajkumarrawalΒ  submitted a paper about 1 month ago
Context-Value-Action Architecture for Value-Driven Large Language Model Agents
AdinaYΒ  submitted a paper about 2 months ago
KAT-Coder-V2 Technical Report
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rajkumarrawalΒ 
posted an update 8 days ago
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2096
LLMs aren’t just answering questions anymore, they’re learning to evolve. Self evolving AI is the true endgame.

AI has shifted from short tasks to long missions. The breakthrough isn’t just automation, it’s machines learning human methods and applying them at machine speed. From cybersecurity to finance, from OPCs to NPCs, the wave is irreversible.

Read the full article: Self Evolving is the Endgame or final destiny

https://huggingface.co/blog/rajkumarrawal/self-evolving-is-the-endgame-or-final-destiny

What’s your definition of true AGI? Comment below.
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rajkumarrawalΒ 
posted an update 16 days ago
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212
I submitted a "Learning to Act and Cooperate for Distributed Black-Box Consensus Optimization" Paper by Zi-Bo Qin, Feng-Feng Wei, Tai-You Chen, Wei-Neng Chen to Daily Papers on huggingface.

A trajectory-driven framework uses large language models to guide agent behavior and cooperation patterns in distributed black-box consensus optimization, improving solution quality and efficiency.

Learning to Act and Cooperate for Distributed Black-Box Consensus Optimization (2605.00691)
rajkumarrawalΒ 
submitted a paper to Daily Papers 16 days ago
Sri-Vigneshwar-DJΒ 
posted an update 18 days ago
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124
![Feather DB LongMemEval Results]( Hawky-ai/longmemeval-results)

We ran Feather DB v0.8.0 on LongMemEval (ICLR 2025) β€” 500 questions across real multi-session conversations, up to 115K tokens each.

**Score: 0.693** Β· GPT-4o full-context baseline: 0.640
Full 500-question run with Gemini-Flash: **$2.40**

Per-axis breakdown:
β†’ Info-extraction: **0.942**
β†’ Knowledge-update: **0.714**
β†’ Multi-session: **0.606**
β†’ Temporal: **0.477** ← the hard one, Phase 9 addresses this

Architecture: Hybrid BM25+dense Β· adaptive temporal decay Β· embedded (no server) Β· p50 = 0.19ms Β· MIT

pip install feather-db

Raw results + audit JSONs: Hawky-ai/longmemeval-results
MElHuseyniΒ 
authored a paper 27 days ago
MElHuseyniΒ 
submitted a paper to Daily Papers 28 days ago
rajkumarrawalΒ 
posted an update about 1 month ago
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1575
I submitted a "Context-Value-Action Architecture for Value-Driven Large Language Model Agents" Paper by TianZe Zhang, Sirui Sun, Yuhang Xie, Xin Zhang Zhiqiang Wu Guojie SongΒ· From
PekingUniversity to Daily Papers on
huggingface .

Large language models exhibit behavioral rigidity that worsens with intensified reasoning, prompting the development of a Context-Value-Action architecture that decouples action generation from cognitive reasoning using a Value Verifier trained on human data.

Context-Value-Action Architecture for Value-Driven Large Language Model Agents (2604.05939)
rajkumarrawalΒ 
submitted a paper to Daily Papers about 1 month ago
rajkumarrawalΒ 
posted an update 3 months ago
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228
I submitted a "Continual GUI Agents" Paper by Ziwei Liu, Borul Kang, Hangjie Yuan, Zixiang Zhao, Wei li, Yifan Zhu, Tao Feng ,
From
Tsinghua ,
ZhejiangUniversity ,
ethz ,
BUPT2023213296 . to Daily Papers on
huggingface .

Continual GUI Agents framework addresses performance degradation in dynamic digital environments through reinforcement fine tuning with novel anchoring rewards that stabilize learning across shifting UI domains and resolutions.

Continual GUI Agents (2601.20732)
Sri-Vigneshwar-DJΒ 
posted an update 4 months ago
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1460
Just released a new dataset designed for training reasoning models on Meta (Facebook/Instagram) advertising fatigue detection!

What is it? A GRPO (Group Relative Policy Optimization) training dataset with 200+ carefully crafted scenarios covering:

πŸ” Fatigue Signal Detection: CTR drops, CPM spikes, frequency analysis
🩺 Performance Diagnosis: Root cause analysis frameworks
πŸ“‹ Strategy: Creative refresh cadence, testing frameworks
πŸ“Š Analysis: ROI calculations, metric interpretation
Why GRPO? GRPO training helps models learn structured reasoning. Each response follows the <thinking> and <answer> format.

Check it out here: Sri-Vigneshwar-DJ/meta-fatigue-grpo-dataset
rajkumarrawalΒ 
posted an update 4 months ago
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3692
I submitted a "FlashLabs Chroma 1.0: A Real-Time End-to-End Spoken Dialogue Model with Personalized Voice Cloning" Paper by Tanyu Chen, Tairan Chen, Kai shen , Zhenghua Bao, Zhihui Zhang, Man Yuan, Yi Shi From
FlashLabs to Daily Papers on
huggingface .

Chroma 1.0 enables real time spoken dialogue with personalized voice cloning through discrete speech representations and interleaved text audio token scheduling.

Chroma 1.0 , the world’s first open source, real time speech to speech model with voice cloning.

FlashLabs Chroma 1.0: A Real-Time End-to-End Spoken Dialogue Model with Personalized Voice Cloning (2601.11141)
rajkumarrawalΒ 
submitted a paper to Daily Papers 4 months ago
Sri-Vigneshwar-DJΒ 
posted an update 4 months ago
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241
πŸ™οΈ Hugging Face Community Post
Title: 🧬 Experimenting with "Dynamic Chaos" in Tamil SLMs

Hi everyone! I just published a new experimental study on Small Language Model (SLM) resilience.

I took the Qwen2.5-0.5B model and put it through a "Chaos Phase" to see how much weight data a tiny model can lose before its understanding of classical Tamil grammar breaks.

Key highlights of the study:

Target Data: Fine-tuned on the Thirukkural (1,330 couplets + modern explanations).
The Chaos Step: Applied 20% random weight pruning but implemented "Layer Protection" for the Token Embeddings and LM Head to keep the characters readable.
Compression: 4-bit (Q4_K_M) quantization for extreme efficiency.
Result: A surrealist classical Tamil model that is ultra-light (~300MB) and ultra-fast!

Check out the model and the experiment logic here: Sri-Vigneshwar-DJ/qwen-tamil-chaos-v1
rajkumarrawalΒ 
submitted 3 papers to Daily Papers 4 months ago
rajkumarrawalΒ 
posted an update 4 months ago
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865
I submitted a "AgencyBench: Benchmarking the Frontiers of Autonomous Agents in 1M-Token Real-World Contexts" Paper by @weizhihao1KeyuLi Junhao shi @dqwangDequan Wang @YangXiao-nlpYang Xiao Mohan Jiang @Sunshine279Jie Sun Yunze Wu Shijie Xia Xiaojie Cai Tianze Xu Weiye Si Wenjie Li Pengfei Liu From
SJTU Shanghai Jiao Tong University
PolyUHK The Hong Kong Polytechnic University GAIRSII-GAIR to Daily Papers on huggingfaceHugging Face.

A potentially another direction for Benchmarking the Frontiers of Autonomous Agents in 2026

Some of the observations founded are :-

-- Long-horizon tasks remain challenging :
Even frontier models struggle with sustained reasoning over real world tasks that require 1M tokens and 90 tool calls, indicating limits in long context autonomy.

-- Proprietary models outperform open source models:
Closed source models achieve a higher average score (48.4%) than open source counterparts (32.1%), revealing a persistent performance gap on complex agentic tasks.

-- Feedback driven self correction varies widely:
Models like GPT 5.2 and Claude show strong gains from iterative feedback, while others (e.g. DeepSeek V3.2) exhibit minimal or no improvement after feedback.

-- Efficiency trade offs are significant:
High performing models often consume far more tokens and time, some models (e.g. Grok 4.1 Fast) are more token efficient despite lower absolute scores.

-- Agentic scaffolds strongly influence performance:
Models tend to perform best within their native or optimized ecosystems, highlighting that agent performance depends on tight coupling between the model and its scaffold not the model alone.

..... many more...

AgencyBench: Benchmarking the Frontiers of Autonomous Agents in 1M-Token Real-World Contexts (2601.11044)
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MElHuseyniΒ 
authored 2 papers 4 months ago
MElHuseyniΒ 
submitted a paper to Daily Papers 4 months ago