π HISTORICAL ARCHIVE - November 02, 2025
            
            
                What was happening in AI on 2025-11-02
            
            
         
        
        
        
            π You are visitor #47291 to this AWESOME site! π
            Archive from: 2025-11-02 | Preserved for posterity β‘
        
        
        
        
        
        
        
        
        
            π Filter by Category
            
                
            
            
                Loading filters...
            
         
        
        
        
            
            
            
            
            
            
            
            
            
            
            
            
                π¬ RESEARCH
                
                
                    
                    
                    
                    
                        
                        
                            
                                πΊ 1 pts
                            
                            
                            
                                β‘ Score: 8.3
                            
                        
                     
                    
                    
                    
                    
                
             
            
            
            
            
            
            
                π οΈ TOOLS
                
                
                    
                    
                    
                    
                        
                        
                            
                                πΊ 314 pts
                            
                            
                            
                                β‘ Score: 8.2
                            
                        
                     
                    
                    
                    
                    
                    
                        
                        
                        π― Coding agent debugging β’ AI-first problem solving β’ CLI-based automation
                        
                        
                        
                        
                            
                            
                            π¬ "AI First. If you really want to understand what the limitations are of the current frontier models (and also really learn how to use them), ask the AI first."
                             β’ "Using coding agents to track down the root cause of bugs like this works really well: Three out of three one-shot debugging hits with no help is extremely impressive."
                            
                        
                        
                     
                    
                
             
            
            
            
            
            
            
                π οΈ TOOLS
                
                
                    
                    
                    
                    
                        
                        
                            
                            
                                β¬οΈ 22 ups
                            
                            
                                β‘ Score: 7.8
                            
                        
                     
                    
                    
                    
                        
                        
                            
                            
                            "Hey r/LocalLLaMA,
I'm the creator of 
LocalAI, and I'm stoked to share our v3.7.0 release.
Many of you already use LocalAI as a self-hosted, OpenAI-compatible API frontend for your GGUF models (via `llama.cpp`), as well as other backends like `vLLM`, `MLX`, etc."
                        
                    
 
                    
                    
                    
                
             
            
            
            
            
            
            
                π¬ RESEARCH
                
                
                    
                    
                    
                    
                        
                            
                                
                                
                                    
                                        via Arxiv
                                    
                                
                            
                            
                                π€  Kimi Team, Yu Zhang, Zongyu Lin et al.
                            
                            π
 2025-10-30
                         
                        
                            
                            
                            
                                β‘ Score: 7.3
                            
                        
                     
                    
                    
                    
                        
                        
                            "We introduce Kimi Linear, a hybrid linear attention architecture that, for
the first time, outperforms full attention under fair comparisons across
various scenarios -- including short-context, long-context, and reinforcement
learning (RL) scaling regimes. At its core lies Kimi Delta Attention (KDA)..."
                        
                    
                    
                    
                    
                
             
            
            
            
            
            
            
                π οΈ TOOLS
                
                
                    
                    
                    
                    
                        
                        
                            
                                πΊ 1 pts
                            
                            
                            
                                β‘ Score: 7.3
                            
                        
                     
                    
                    
                    
                    
                
             
            
            
            
            
            
            
                π SECURITY
                
                
                    
                    
                    
                    
                        
                        
                            
                                πΊ 2 pts
                            
                            
                            
                                β‘ Score: 7.2
                            
                        
                     
                    
                    
                    
                    
                
             
            
            
            
            
            
            
                π¬ RESEARCH
                
                
                    
                    
                    
                    
                        
                            
                                
                                
                                    
                                        via Arxiv
                                    
                                
                            
                            
                                π€ Anushka Sivakumar, Andrew Zhang, Zaber Hakim et al.
                            
                            π
 2025-10-30
                         
                        
                            
                            
                            
                                β‘ Score: 7.2
                            
                        
                     
                    
                    
                    
                        
                        
                            "This work introduces SteerVLM, a lightweight steering module designed to
guide Vision-Language Models (VLMs) towards outputs that better adhere to
desired instructions. Our approach learns from the latent embeddings of paired
prompts encoding target and converse behaviors to dynamically adjust
activ..."
                        
                    
                    
                    
                    
                
             
            
            
            
            
            
            
                π οΈ SHOW HN
                
                
                    
                    
                    
                    
                        
                        
                            
                                πΊ 309 pts
                            
                            
                            
                                β‘ Score: 7.2
                            
                        
                     
                    
                    
                    
                    
                    
                        
                        
                        π― LLM capabilities and limitations β’ Future of software development β’ Transformation of user experience
                        
                        
                        
                        
                            
                            
                            π¬ "LLMs can churn out SPAs but struggle with domain-specific tasks"
                             β’ "LLMs can't implement RAFT consensus correctly"
                            
                        
                        
                     
                    
                
             
            
            
            
            
            
            
                π¬ RESEARCH
                
                
                    
                    
                    
                    
                        
                            
                                
                                
                                    
                                        via Arxiv
                                    
                                
                            
                            
                                π€ Biao Zhang, Yong Cheng, Siamak Shakeri et al.
                            
                            π
 2025-10-30
                         
                        
                            
                            
                            
                                β‘ Score: 7.1
                            
                        
                     
                    
                    
                    
                        
                        
                            "Recent large language model (LLM) research has undergone an architectural
shift from encoder-decoder modeling to nowadays the dominant decoder-only
modeling. This rapid transition, however, comes without a rigorous comparative
analysis especially \textit{from the scaling perspective}, raising concer..."
                        
                    
                    
                    
                    
                
             
            
            
            
            
            
            
                π€ AI MODELS
                
                
                    
                    
                    
                    
                        
                        
                            
                            
                                β¬οΈ 4 ups
                            
                            
                                β‘ Score: 7.0
                            
                        
                     
                    
                    
                    
                        
                        
                            
                            
                                
                            
                            "Iβm excited to share **Part 3** of my series on building an LLM *from scratch*. 
  
This installment dives into the guts of model architecture, multi-GPU training, memory-precision tricks, checkpointing & inference.
**What youβll find inside:**
* Two model sizes (117M & 354M parameters) a..."
                        
                    
                    
                    
                    
                
             
            
            
            
            
            
                
                    π‘ AI NEWS BUT ACTUALLY GOOD
                    The revolution will not be televised, but Claude will email you once we hit the singularity.
                    
                    
                    
                    Get the stories that matter in Today's AI Briefing.
                    
                    Powered by Premium Technology Intelligence Algorithms β’ Unsubscribe anytime
                 
             
            
            
            
                βοΈ ETHICS
                
                
                    
                    
                    
                    
                        
                        
                            
                                πΊ 3 pts
                            
                            
                            
                                β‘ Score: 7.0
                            
                        
                     
                    
                    
                    
                    
                
             
            
            
            
            
            
            
                π¬ RESEARCH
                
                
                    
                    
                    
                    
                        
                            
                                
                                
                                    
                                        via Arxiv
                                    
                                
                            
                            
                                π€ Mantas Mazeika, Alice Gatti, Cristina Menghini et al.
                            
                            π
 2025-10-30
                         
                        
                            
                            
                            
                                β‘ Score: 6.9
                            
                        
                     
                    
                    
                    
                        
                        
                            "AIs have made rapid progress on research-oriented benchmarks of knowledge and
reasoning, but it remains unclear how these gains translate into economic value
and automation. To measure this, we introduce the Remote Labor Index (RLI), a
broadly multi-sector benchmark comprising real-world, economical..."
                        
                    
                    
                    
                    
                
             
            
            
            
            
            
            
                π¬ RESEARCH
                
                
                    
                    
                    
                    
                        
                            
                                
                                
                                    
                                        via Arxiv
                                    
                                
                            
                            
                                π€ Zixu Shen, Kexin Chu, Yifan Zhang et al.
                            
                            π
 2025-10-30
                         
                        
                            
                            
                            
                                β‘ Score: 6.9
                            
                        
                     
                    
                    
                    
                        
                        
                            "The expansion of large language models is increasingly limited by the
constrained memory capacity of modern GPUs. To mitigate this,
Mixture-of-Experts (MoE) architectures activate only a small portion of
parameters during inference, significantly lowering both memory demand and
computational overhea..."
                        
                    
                    
                    
                    
                
             
            
            
            
            
            
            
                π¬ RESEARCH
                
                
                    
                    
                    
                    
                        
                        
                            
                                πΊ 2 pts
                            
                            
                            
                                β‘ Score: 6.8
                            
                        
                     
                    
                    
                    
                    
                
             
            
            
            
            
            
            
                π οΈ SHOW HN
                
                
                    
                    
                    
                    
                        
                        
                            
                                πΊ 1 pts
                            
                            
                            
                                β‘ Score: 6.8
                            
                        
                     
                    
                    
                    
                    
                
             
            
            
            
            
            
            
                π¬ RESEARCH
                
                
                    
                    
                    
                    
                        
                            
                                
                                
                                    
                                        via Arxiv
                                    
                                
                            
                            
                                π€ Zhichao Wang, Dongyang Ma, Xinting Huang et al.
                            
                            π
 2025-10-30
                         
                        
                            
                            
                            
                                β‘ Score: 6.8
                            
                        
                     
                    
                    
                    
                        
                        
                            "The "end-to-end" label for LLMs is a misnomer. In practice, they depend on a
non-differentiable decoding process that requires laborious, hand-tuning of
hyperparameters like temperature and top-p. This paper introduces AutoDeco, a
novel architecture that enables truly "end-to-end" generation by lear..."
                        
                    
                    
                    
                    
                
             
            
            
            
            
            
            
                π¬ RESEARCH
                
                
                    
                    
                    
                    
                        
                            
                                
                                
                                    
                                        via Arxiv
                                    
                                
                            
                            
                                π€ Mehar Bhatia, Shravan Nayak, Gaurav Kamath et al.
                            
                            π
 2025-10-30
                         
                        
                            
                            
                            
                                β‘ Score: 6.7
                            
                        
                     
                    
                    
                    
                        
                        
                            "As LLMs occupy an increasingly important role in society, they are more and
more confronted with questions that require them not only to draw on their
general knowledge but also to align with certain human value systems.
Therefore, studying the alignment of LLMs with human values has become a
crucia..."
                        
                    
                    
                    
                    
                
             
            
            
            
            
            
            
                π οΈ SHOW HN
                
                
                    
                    
                    
                    
                        
                        
                            
                                πΊ 3 pts
                            
                            
                            
                                β‘ Score: 6.7
                            
                        
                     
                    
                    
                    
                    
                
             
            
            
            
            
            
            
                π¬ RESEARCH
                
                
                    
                    
                    
                    
                        
                            
                                
                                
                                    
                                        via Arxiv
                                    
                                
                            
                            
                                π€ William Overman, Mohsen Bayati
                            
                            π
 2025-10-30
                         
                        
                            
                            
                            
                                β‘ Score: 6.7
                            
                        
                     
                    
                    
                    
                        
                        
                            "As increasingly capable agents are deployed, a central safety question is how
to retain meaningful human control without modifying the underlying system. We
study a minimal control interface where an agent chooses whether to act
autonomously (play) or defer (ask), while a human simultaneously choose..."
                        
                    
                    
                    
                    
                
             
            
            
            
            
            
            
                π’ BUSINESS
                
                
                    
                    
                    
                    
                        
                        
                            
                            
                                β¬οΈ 492 ups
                            
                            
                                β‘ Score: 6.7
                            
                        
                     
                    
                    
                    
                    
                    
                    
                    
                        
                        
                        π― Sam Altman's credibility β’ OpenAI's performance β’ Hallucination vs. lying
                        
                        
                        
                        
                            
                            
                            π¬ "Sam Altman, who publicly lies all the time, is a liar? Shocking"
                             β’ "I bet Sam is constantly taking credit for other people's work."
                            
                        
                        
                     
                    
                
             
            
            
            
            
            
            
                π¬ RESEARCH
                
                
                    
                    
                    
                    
                        
                            
                                
                                
                                    
                                        via Arxiv
                                    
                                
                            
                            
                                π€ Penghui Qi, Zichen Liu, Xiangxin Zhou et al.
                            
                            π
 2025-10-30
                         
                        
                            
                            
                            
                                β‘ Score: 6.7
                            
                        
                     
                    
                    
                    
                        
                        
                            "Reinforcement learning (RL) fine-tuning of large language models (LLMs) often
suffers from instability due to the numerical mismatch between the training and
inference policies. While prior work has attempted to mitigate this issue
through algorithmic corrections or engineering alignments, we show t..."
                        
                    
                    
                    
                    
                
             
            
            
            
            
            
            
                π¬ RESEARCH
                
                
                    
                    
                    
                    
                        
                            
                                
                                
                                    
                                        via Arxiv
                                    
                                
                            
                            
                                π€ Hyunji Lee, Minseon Kim, Chinmay Singh et al.
                            
                            π
 2025-10-30
                         
                        
                            
                            
                            
                                β‘ Score: 6.6
                            
                        
                     
                    
                    
                    
                        
                        
                            "As coding agents are increasingly deployed in large codebases, the need to
automatically design challenging, codebase-level evaluation is central. We
propose Gistify, a task where a coding LLM must create a single, minimal,
self-contained file that can reproduce a specific functionality of a codebas..."
                        
                    
                    
                    
                    
                
             
            
            
            
            
            
            
                π¬ RESEARCH
                
                
                    
                    
                    
                    
                        
                            
                                
                                
                                    
                                        via Arxiv
                                    
                                
                            
                            
                                π€ Zewen Chi, Li Dong, Qingxiu Dong et al.
                            
                            π
 2025-10-30
                         
                        
                            
                            
                            
                                β‘ Score: 6.5
                            
                        
                     
                    
                    
                    
                        
                        
                            "We envision a new era of AI, termed agentic organization, where agents solve
complex problems by working collaboratively and concurrently, enabling outcomes
beyond individual intelligence. To realize this vision, we introduce
asynchronous thinking (AsyncThink) as a new paradigm of reasoning with lar..."
                        
                    
                    
                    
                    
                
             
            
            
            
            
            
            
                π‘οΈ SAFETY
                
                
                    
                    
                    
                    
                        
                        
                            
                                πΊ 4 pts
                            
                            
                            
                                β‘ Score: 6.4
                            
                        
                     
                    
                    
                    
                    
                
             
            
            
            
            
            
            
                π οΈ TOOLS
                
                
                    
                    
                    
                    
                        
                        
                            
                                πΊ 243 pts
                            
                            
                            
                                β‘ Score: 6.3
                            
                        
                     
                    
                    
                    
                    
                    
                        
                        
                        π― Difficulty with CLAUDE.md instructions β’ Potential for improved tooling β’ Comparing CLI agents vs Cursor
                        
                        
                        
                        
                            
                            
                            π¬ "I can't get Claude to follow something as simple as that!"
                             β’ "One solution would be to script it and have it run pre commit to regenerate the CLAUDE.md with the new paths."
                            
                        
                        
                     
                    
                
             
            
            
            
            
            
            
                π¬ RESEARCH
                
                
                    
                    
                    
                    
                        
                        
                            
                            
                                β¬οΈ 12 ups
                            
                            
                                β‘ Score: 6.2
                            
                        
                     
                    
                    
                    
                        
                        
                            
                            
                                
                            
                            "
https://preview.redd.it/h8ax4n36ktyf1.png?width=1080&format=png&auto=webp&s=e1c08e0c0415264d29d72b495a725f857a5fb56e
*Authors:*Β Vladyslav Moroshan, Julien Siems, Arber Zela, Timur Carstensen,Β Frank Hutter
TempoPFN is a univariate time series foundation model based on linear RNNs that i..."
                        
                    
 
                    
                    
                    
                
             
            
            
            
            
            
            
                π¬ RESEARCH
                
                
                    
                    
                    
                    
                        
                        
                            
                                πΊ 1 pts
                            
                            
                            
                                β‘ Score: 6.1
                            
                        
                     
                    
                    
                    
                    
                
             
            
            
            
            
            
            
                π¬ RESEARCH
                
                
                    
                    
                    
                    
                        
                            
                                
                                
                                    
                                        via Arxiv
                                    
                                
                            
                            
                                π€ Arnab Sen Sharma, Giordano Rogers, Natalie Shapira et al.
                            
                            π
 2025-10-30
                         
                        
                            
                            
                            
                                β‘ Score: 6.1
                            
                        
                     
                    
                    
                    
                        
                        
                            "We investigate the mechanisms underlying a range of list-processing tasks in
LLMs, and we find that LLMs have learned to encode a compact, causal
representation of a general filtering operation that mirrors the generic
"filter" function of functional programming. Using causal mediation analysis on
a..."
                        
                    
                    
                    
                    
                
             
            
            
            
            
            
            
                π¬ RESEARCH
                
                
                    
                    
                    
                    
                        
                        
                            
                                πΊ 1 pts
                            
                            
                            
                                β‘ Score: 6.1