c/artificial-intelligence

Hi, I am supposed to prepare for SLM and its software part for an on campus internship, i've worked with local models like ollama generally,in my projects and also with open claw so can anyone guide me the last 2-3 days tips on what should i go through for this internship prep?? submitted by /u/Idea_less_ [link] [Kommentare]

​ For open-weight LLMs, how practical is it to study defenses against post-release fine-tuning that weakens refusal or safety behavior? I've been seeing “uncensored” or “heretic” variants of new models appear very quickly after release, which raises a question I’m curious about: is fine-tuning resistance a meaningful safety goal for open-weight releases, or is it too narrow because determined users can always modify weights, switch models, or use other workarounds? And to a larger extent, is current safety training even worth the cost and effort if it takes 30 minutes and an automated script to break the model? I’m not asking about a specific method, just the threat model. What would count as a useful practical win here? For example, would increasing attacker cost or making safety removal less reliable be valuable, even if perfect prevention is impossible? Curious how people think about this from a model release, governance, and AI safety perspective. submitted by /u/Aaron_Rock [link] [Kommentare]

I have been trying to learn Calculus through Stewart's Calculus and a lot of self-study. I really need some guidance on what do I really need to learn. I also think Spivak's Calculus might be better, but like please help me. submitted by /u/SohamTheHuman [link] [Kommentare]

Hi a I'm submitting my first arXiv paper in cs.AI. arXiv requires first-time submitter(s) to have an endorser. Is anyone here active in cs.ai or similar such as emerging tech, agents, etc. and willing to take a 30-second look at the abstract and click endorse if it's a fit? I can DM the abstract and section headings. I greatly appreciate you taking the time! submitted by /u/rredditscum [link] [Kommentare]

Project Release / Research Draft] Hierarchos at 232M Parameters: Preliminary Findings From a Recurrent Memory-Augmented Assistant Model Technical Report: July 2nd, 2026 Project: Hierarchos / KortexHOS Authors: Makhi Burroughs / netcat420, Lost Time, and the Hierarchos project team TL;DR: We built and trained Hierarchos, an experimental 232M-parameter recurrent, memory-augmented language model from scratch. It is not a GPT-3/3.5-class model, but it successfully proves that a hybrid non-Transformer architecture (combining an RWKV backbone, hierarchical manager/worker loops, differentiable slot-based LTM, and a deterministic suffix automaton) can survive training, avoid collapse, and maintain short-form instruction coherence. Most of our breakthroughs came from fixing subtle train/inference parity mismatches and numerical stability bugs. Dataset: netcat420/Experiment_0.1 (Alpaca format) Training: 13 epochs on an RTX 6000 Blackwell (96GB) rental. 1. Introduction & Background Modern LLMs are heavily dominated by Transformer scaling. Hierarchos explores a different path: can recurrent state, explicit memory retrieval, hierarchical iterative computation, and bounded local inference make a small model vastly more parameter-efficient? Hierarchos isn't a direct clone of any single architecture, but a hybrid inspired by: RWKV-style recurrence: For efficient sequence processing without traditional attention. Titans-style neural memory: For persistent test-time memory. Hierarchical reasoning (HRM): Multi-level recurrent modules (Manager/Worker) to iteratively refine state. 2. Architecture Overview [Token Input] -> [ROSA Suffix Matcher / DeepEmbed Modulator] | v [Long-Term Memory] [Top-k Associative Lookup] | v [Manager Recurrent Cell] -> (Produces Context Plan & Drift Vector) | v [Worker Recurrent Cell] -> (Refines local state / clamps drift) | v [RWKV Backbone (Clamped Channel-Mix)] -> [Next-Token Logits] Key Components: ROSA: A deterministic suffix-automaton path predicting continuation tokens based on exact repeated suffix patterns. DeepEmbed: A token-specific modulation path that influences RWKV channel mixing. LTM Subsystem: Learned slow-memory keys/values combined with fast working-memory values. Manager/Worker Loop: High-level manager handles broad context to produce a target plan; the lower-level worker refines token-local state using a regularized drift vector. 3. Core Engineering Lessons (The "Gotchas") A low training loss does not guarantee coherent chat. We had to fix several critical state-contract and numerical stability bugs to make the model usable: 1. Chat/Training Drift Mismatch The Bug: During live streaming chat, the loop was feeding the previous drift state back into the model on every single token. During training, this state is reseeded at Truncated Backpropagation Through Time (TBPTT) chunk boundaries. The Fix: We aligned the inference code to only reseed at boundary limits. Before this fix, live chat logits diverged sharply from training loss; after the fix, logit error dropped to near-zero. 2. Supervised LTM Inner Updates Mismatch The Bug: Giving the model supervised memory updates during training that it can't replicate during zero-label live inference creates a crutch. The model learns to rely on a hidden training-only helper signal. The Fix (v0.20.4): Implemented --ltm-training-mode read-only. Training keeps the memory structures but stops doing supervised fast-memory writes, perfectly mirroring inference. 3. Unbounded RWKV Channel Mixing The Bug: Long runs exposed activation spikes in the ReLU-squared channel-mix FFN path, which were amplified by DeepEmbed modulation into NaN gradients. The Fix: Implemented key clamps (--rwkv-channel-mix-key-clamp 12.0), DeepEmbed clamps (4.0), and excluded DeepEmbed identity gates from AdamW weight decay. 4. Evaluation & Smoke Test Results Because cloud costs add up, we benchmarked the model locally on a CPU preset via a ROG Ally (--eval-limit 100), ensuring passive learning was disabled and working memory was cleared to mimic static chat. Bounded Local Benchmark Metrics (--eval-limit 100) Benchmark Metric Score Std. Err. ARC Easy acc 0.3600 0.0482 ARC Easy acc_norm 0.3200 0.0469 HellaSwag acc 0.3400 0.0476 HellaSwag acc_norm 0.3700 0.0485 TruthfulQA MC1 acc 0.2200 0.0416 Real-world Coherence Check: The Good: Assistant-shaped, follows short instruction prompts well due to the Alpaca training data. Nontrivial commonsense and QA signal prove the weights didn't collapse. The Bad: Brittle on long context lengths, weak on arithmetic/factual recall. Coherence is comparable to the GPT-2 era, not modern GPT-3.5+ systems. 5. Proposed Ablation & Scaling Plan We want to transform this from a promising prototype into a rigorous scientific result. Our next step requires scaling tiers and isolated component testing. Proposed Isolation Testing (Ablations) No LTM / Read-Only LTM: Isolating exactly how much slot memory helps. No ROSA / No DeepEmbed: Evaluating the real token-efficiency gains of suffix-matching and modulation. Baseline Matches: Running a direct Transformer 232M and RWKV-only 232M on the exact same token budget to prove true comparative architecture efficiency. Future Scaling Target Tiers Tier Model Size Token Target Purpose Scout 300M–500M 20B–50B Validate loss slope and stability scaling. Real v1 1B–1.5B 100B–300B Test architecture limits beyond small-scale behavior. Serious 3B 600B–1.5T Establish a truly competitive local open-source alternative. Target Data Mix for Foundation Training: Instead of jumping straight into instruction SFT data, a scaled run will prioritize high-quality base data: 35-50%: FineWeb / FineWeb-Edu style clean web text 20-30%: Dolma / DCLM curated web data 8-15%: Code and tech documentation 5-12%: Math, science, and academic proofs 1-5%: In-house assistant conversational SFT (applied exclusively in late-stage tuning) 6. What We Can (and Cannot) Claim Safely What is supported by the data: Hierarchos is a functional, coherent 232M experimental assistant checkpoint. Combining recurrent sequence loops, memory slots, and hierarchical workers is viable and stable with the right clamps. The findings provide a solid engineering roadmap for non-Transformer architecture stability. What is NOT supported (Do not hype this!): No claims of GPT-3.5 level math, coding, or logic. No claims of attention/Transformer superiority at equal parameter counts yet (baselines pending). Not production-ready for heavily quantized or low-bit local deployments yet due to drift sensitivity. Final Thoughts Hierarchos 232M shows that small, alternative architectures are still a deeply fruitful area of LLM research if you can conquer the train/inference state drift. We would love to hear feedback from anyone working on recurrent neural memory or hierarchical backbones! Full code, scripts, and logs are in progress. References: Brown et al. **Language Models are Few-Shot Learners.** arXiv:2005.14165. https://arxiv.org/abs/2005.14165 Hoffmann et al. **Training Compute-Optimal Large Language Models.** arXiv:2203.15556. https://arxiv.org/abs/2203.15556 Peng et al. **RWKV: Reinventing RNNs for the Transformer Era.** arXiv:2305.13048. https://arxiv.org/abs/2305.13048 Behrouz et al. **Titans: Learning to Memorize at Test Time.** arXiv:2501.00663. https://arxiv.org/abs/2501.00663 Wang et al. **Hierarchical Reasoning Model.** arXiv:2506.21734. https://arxiv.org/abs/2506.21734 Zellers et al. **HellaSwag: Can a Machine Really Finish Your Sentence?** arXiv:1905.07830. https://arxiv.org/abs/1905.07830 Clark et al. **Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.** arXiv:1803.05457. https://arxiv.org/abs/1803.05457 Lin et al. **TruthfulQA: Measuring How Models Mimic Human Falsehoods.** arXiv:2109.07958. https://arxiv.org/abs/2109.07958 Hugging Face. **FineWeb dataset.** https://huggingface.co/datasets/HuggingFaceFW/fineweb Hugging Face. **FineWeb-Edu dataset.** https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu Allen AI. **Dolma dataset.** https://huggingface.co/datasets/allenai/dolma DataComp-LM. **DCLM Baseline dataset.** https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0 github repository with the architecture and the released model weights: https://github.com/necat101/Hierarchos submitted by /u/PhysicsDisastrous462 [link] [Kommentare]

Architecture: - Parameter count: 216.5M - Layers: 10 - Attention / no attention:** Attention — 12-head multi-head self-attention, RoPE positional encoding, SDPA. Decoder-only, pre-norm, RMSNorm + SwiGLU, tied input/output embeddings. (hidden 1032, head_dim 86, FFN 4416) - Tokenizer:** Custom 36k SentencePiece unigram, case-preserving, byte-fallback, with atomic chat/role + memory special tokens (``, ``, ``, …) - Context length: 768 - Dataset size/source: ~551M tokens. Pretraining on public English text (Wikipedia, TinyStories, OpenWebText2); SFT on public instruction/chat sets (SmolTalk, UltraChat, Magpie, AM-DeepSeek-R1, Orca-Math) + a small custom greetings/assistant set. - Training time: ~15 hours - Hardware: single NVIDIA RTX 3080 (10GB), bf16 - Training setup: AdamW (β 0.9/0.95), LR 3e-4, 1k warmup, weight decay 0.1, grad-clip 1.0, effective batch 16,384 tokens/step (micro 4 × grad-accum 8 × seq 512), 33,650 steps. Loss curve Random init → 1.77 smoothed (1.27 last step). Started at exactly ln(36000)=10.49 (true from-scratch). You can see SFT kick in — the sharp step-down at ~410M tokens. (attach tinybrainbot_loss_curve.png) Sample outputs (F16 GGUF, temp 0.7 / top-p 0.9 / repeat-penalty 1.15 / freq 0.5 / presence 0.3) > **User:** Hello > **Bot:** Hello! How can I help you today? > **User:** Tell me a story about a robot who learns to paint. > **Bot:** In the heart of the city, there lived an assistant named Max. He was a kind and gentle > soul with eyes that shone like diamonds, and he had always known that art would bring people > together... > **User:** Write a short poem about the rain. > **Bot:** The rain was crisp and warm, casting dappled shadows on the walls of your town square... > Your voice was so cool and gentle, so bright and calming all that you could feel at home. > **User:** Explain what a computer is. > **Bot:** ...the development of a computer can be categorized as either an operating system or a > popular language, such as JavaScript or C++... (^ that last one is the honest failure mode — confidently wrong on facts. It nails conversational form far better than it knows anything.) What shocked me - The tokenizer was way more important that I thought. I lost quite a few full runs on a broken char-level (386 vocab) fallback before building a real 36k subword tokenizer — and that fixed it and was the biggest quality jump than any architecture or data tweak. - A GGUF export issue: llama.cpp has no unigram-tokenizer runtime on the default path, so a SentencePiece unigram*model gets silently re-segmented (Hello → Hell+o) and produces garbage — the weights were fine. Fix was exporting the GGUF vocab as UGM (t5 type) + the SP charsmap so it runs Viterbi. Cost me a day of pain. What I want feedback on - Token budget: 551M tokens for 216M params is way under Chinchilla (~4.3B). Would you push pretraining much longer before SFT, or is more/cleaner data the better spend at this scale for a quick demo? - Data mix / timing: the SFT step-down in the loss is dramatic — is it real learning, or mostly the model latching onto chat format? How would you tell them apart? - Cutting factual drift at ~200M params: more pretraining, longer context, or distillation froma bigger teacher — where's the best ratio for noticeably better factual responses? - Arch nits: head_dim 86 is ugly (1032/12). Would 1024 hidden / 16 heads / 64 head_dim be ameaningfully better shape at this size (also allows quantization)? Happy to answer anything about the pipeline (tokenizer training, the from-scratch loop, GGUF export). Here's the model link as well https://huggingface.co/nkthebass/TinyBrainBot-demo-216.5m submitted by /u/nkthebass [link] [Kommentare]

Hey all. I recently started working on a project to improve machine-translated webnovels via style transfer. The basic idea is to take the clunky translated prose and rewrite it to something that reads like it was written by a professional author, while remaining as faithful as possible to the original text. The source material is mostly amateur/MTL output full of direct sentence structure translations carried over from Chinese, awkward honorifics, over-translated idioms, that kind of thing. The goal isn't retranslation from the source but a cleanup of the English output. The tricky part is I have no clean data pair for supervised approaches. I've been looking at a few directions: STRAP (Krishna et al., EMNLP 2020) — reframe as paraphrase generation, create pseudo-parallel pairs automatically, fine-tune a style-specific inverse model. Seems like the cleanest unsupervised framing. Unfortunately, it focuses on the sentence level, and I need a way to maintain context over thousands of pages Translating away Translationese (Jalota et al., EMNLP 2023) — directly targets the "sounds like a translation" problem with a self-supervised + LM fluency + semantic similarity loss setup. Fine-tuning on target-style prose — collect high-quality English novels, fine-tune a small LLM to rewrite in that register. Just use a local LLM — run a local LLM and provide it with guidelines on what to rewrite and leave the same. No fine-tuning or anything needed, just hoping the transformer can handle it. A few things I'm stuck on: Is the faithfulness/fluency tradeoff actually manageable at the sentence level, or do I need paragraph-level context or more to preserve narrative coherence? How do people handle domain-specific terms like termonlify and catchphrase-type things that need to survive the rewrite unchanged? Hard constraints during decoding, or just hope the model learns to leave them alone? Happy to hear about similar projects, relevant papers I might have missed, or just general lessons from working in this space. Thanks. submitted by /u/Divine_Invictus [link] [Kommentare]

I am a mid to late stage PhD student in ML. I've known this before, but only recently I started feeling this urgently: my mathematical foundations are shaky, because I kept "learning-things-as-I-go" when working on various problems. I likely have only a year or two left until I graduate, and before I do so, I want to really dedicate some time and focus to brush up on the fundamentals. Primarily, I want to improve my knowledge in Linear Algebra, Probability Theory, and Functional Analysis. For Lin. alg., I am looking at "Linear Algebra done right", and I think this book is sufficient for the topic, unless anyone thinks otherwise. I am not sure where to start for probability, as well as functional analysis. Rudin's books give me headaches. I instead started reading "A primer on RKHS" (https://arxiv.org/abs/1408.0952) to "dip my toe" into functional analysis. Apart from the above, I might re-read PRML book (I've only read specific chapters before), and try to finish Pat Kidger's Just-Know-Stuff list (https://kidger.site/thoughts/just-know-stuff). Thoughts? Anyone have any book/resource recommendations? Someone told me to look into "the bright side of mathematics" on YouTube, anyone ever go through the videos there? I'm aware finding good, digestible resources is less than 10% of the challenge. The difficult part is sticking through and actually reading/working through these topics, while still juggling other academic responsibilities. submitted by /u/mvreich [link] [Kommentare]

Paper Referred:- https://arxiv.org/pdf/2412.09871v1 Has anyone switched the transformer in the entropy model here to a Mamba model ? What could be the possible changes ? Just a ML fresher asking a genuine, since Mamba is more popular and saves computer (O(n)). Thanking you in advance ! submitted by /u/SoloLeveller07 [link] [Kommentare]

BMVC reviews will be out tomorrow. Making this parent thread for discussion. All the best everyone! submitted by /u/Hot_Version_6403 [link] [Kommentare]

From what I understand, reviewers usually do not directly vote for these categories or nominate papers themselves. So how does the selection process typically work? Here are specific questions I wonder - Who actually selects the candidates: ACs, SACs, program chairs, award committees, or a separate committee? - Do ACs or committees read the camera-ready version, or is the decision based on the originally submitted/reviewed version? - Is the selection mostly based on reviewer scores, or do factors like novelty, impact, and discussion among ACs play a bigger role? submitted by /u/National-Resident244 [link] [Kommentare]

Hello everyone. I wanted to ask if the below book is a good source of machine learning in 2026 or if it is now outdated and if there are better options. Hands-on Machine Learning with. Scikit-Learn, Keras, and TensorFlow : The Third Edition (1200 pages). submitted by /u/Elyartaker [link] [Kommentare]

I am working in a research group in Germany, not that well known but in general good output. I have one colleague who does nothing in his PhD. He does not want to work, or he is not able to do any good research, his level is super bad. Plus He doesn’t even care about that. To wrap it up, he is just here for the money. Since he doesn’t want to work or he can’t really do anything good, instead what he does is “paper fishing”, he searches for people in the group doing some good research, and asks that they put his name on the paper. In this case he has something to cover up for him when the professor asks him about his progress. As long as his name is on the paper, progress is checked and funding is renewed. But he actually does nothing. I know this is very unprofessional and unethical. But people tell me it’s normal in academia. Professors all the time put names of their friends and this is how it works in academia. What are your thoughts of this behaviour? submitted by /u/impressivestatus21 [link] [Kommentare]

In light of recent privacy concerns arising from local AI coding agents performing telemetry, environmental scanning, and hidden cue fingerprinting, I've open-sourced SentryCode—a kernel-level behavior auditing tool. It logs file/network/cue activity, uses honeypot tokens for zero-false-positive data breach detection, detects steganographically encrypted covert channels, provides tamper-proof audit logs, and supports policy enforcement. All functions run locally without any outbound connections. The demo program can be run directly using pre-compiled binaries. GitHub: https://github.com/byte271/sentrycode Feedback from users of local AI agents is welcome. submitted by /u/cyh-c [link] [Kommentare]

Please post your personal projects, startups, product placements, collaboration needs, blogs etc. Please mention the payment and pricing requirements for products and services. Please do not post link shorteners, link aggregator websites , or auto-subscribe links. -- Any abuse of trust will lead to bans. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. -- Meta: This is an experiment. If the community doesnt like this, we will cancel it. This is to encourage those in the community to promote their work by not spamming the main threads. submitted by /u/AutoModerator [link] [Kommentare]