标签：#AR

RecursiveMAS (arXiv 2604.25917) showed that agents sharing internal reasoning state outperform agents that share only final outputs. The average accuracy gain across benchmarks was 8.3 points. The mechanism: each agent passes not just its answer but the latent embeddings from its own reasoning process, and the next agent conditions on both. The paper is a good result. The catch is access. RecursiveMAS requires open-weight models with hidden states exposed at inference time. That rules out Claude, GPT-4o, and Gemini. I built a Claude-native version using the Anthropic extended thinking API. The core idea transfers: instead of passing latent vectors, pass the full thinking text. The paper calls it internal state sharing; the Claude version calls it thinking-block relay. The architecture problem Claude's extended thinking blocks carry an encrypted signature tied to the originating conversation. You cannot pass a signed thinking block into a different agent's messages array. The API rejects it. The workaround: extract the text from the thinking block and inject it as a regular user message. # Extract thinking text from Agent 1 thinking_text = next ( ( b . thinking for b in response . content if b . type == " thinking " ), "" ) # Inject into Agent 2 as regular context, not as a thinking block context = f " Prior agent reasoning: \n { thinking_text } " The signature does not transfer. The reasoning does. relay-structured: what I built first The first architecture was a Planner > Critic > Solver loop where each agent emits a compact mental model JSON instead of raw thinking text. Raw thinking at a 1024-token budget is often compressed and fragmented. The hypothesis was that 150 tokens of structured signal carries more information per token than 1024 tokens of compressed prose. The schema each agent emits: { "interpretation" : "how the agent read the problem" , "key_steps" : [ "step 1" , "step 2" ], "rejected_approaches" : [ "approach tried and discarded" ], "confidence" :

I run Sola, a travel app for people who move differently from the traveller the industry was built for. While building it, I kept hitting the same wall. The data I wanted to query did not exist. Not because nobody collected it, but because the schema underneath the whole industry never had a field for it. So on 27 May 2026 I sat down and audited Booking.com. The homepage form, the currency selector, a Bangkok search results page. I wrote down what it accepts and what it refuses. Then I looked at the new AI travel agents shipping on top of it. Here is what I found, and why it matters to anyone building in this space right now. The form is the spec Booking.com's homepage search bar accepts exactly four inputs: A destination, as a single text field A check-in date and check-out date, as one range An occupancy counter, defaulting to "2 adults · 0 children · 1 room" A search button That is the spec. An online travel agency (OTA) is a CRUD app over this spec, and Expedia, Agoda, and Hotels.com run the same four fields. Airbnb lets you skip the dates. The destination stays a single field everywhere. Think about what a spec encodes. The default occupancy is a couple. Not a solo traveller, not a parent with one child, not three generations, not seven people eating from one host's kitchen. The form cannot accept a circuit ("Bangkok, then Hanoi, then Jakarta" forces three separate searches). It cannot accept an open date ("October, not sure which week"). It has no field for the part of a trip where you sleep at family but spend money in restaurants. When you fill that form, you have not searched. You have submitted to a schema. Most of the world's travellers fail the schema before they fail the search. The data receipts I am a builder, so I went for counts, not adjectives. Everything below rendered on the platform on 27 May 2026. Currencies: 52 offered, about 180 in circulation. Eight currencies sit featured at the top of the dropdown. On the day I ran it the order was EUR, US

[ Removed by Reddit on account of violating the content policy . ] submitted by /u/Street-Gate7322 [link] [留言]

Dell, Microsoft, and others are unveiling new laptops to compete directly with the Neo, but not all are learning the right lessons from Apple.

You can’t see AC comments yet, but you can see the Accept/Reject consoles. My paper (with scores of 8,6,3) got rejected. submitted by /u/GeeseChen [link] [留言]

submitted by /u/Mushroom-Severe [link] [留言]

seems that the power markets are not able to keep up with all these demand data centers coming online even with all of the new power plants and renewables coming online. will the grid be able to keep up with all these data centers and will ai developments be affected by it? submitted by /u/FF430 [link] [留言]

You've seen the developer. Maybe you are the developer. They discover DRY — ✨ Don't Repeat Yourself...

So I have a super busy job and I am by far the fastest out of the 3 others who have the same job as me. Problem is I have enough work where i could literally work 70-80 hours a week and still not catch up. Ive been using Chatgpt and Claude to help with my work load and ive found Claude to be much better for my actualy job duties. But Claudes usage caps kill me. I really need the best AI for basically being a work assitant. I need something that can create spreadsheets, analyze data, read emails, sort thru photos and catalog them. Grok was not really any help, Chatgpt is just meh, but ive found Claude to be the best out of what im looking for but again its usage limits kill me and i cannot afford to pay for the overages. Im already a pro user for chatgpt and claude. What AI can do the things im asking the best for the best price and usage? Most important to my work in order of most important to least: Photo cataloging, analyzing data, spreadsheet creation, and summarizing emails. submitted by /u/JumpyChemistry [link] [留言]

Looking for a local AI solution for film dubbing / audio sync correction (offline if possible). I have a foreign movie with an English audio version, but the video is low resolution and the audio timing slowly drifts out of sync over time. If I manually align it at the start, it gradually becomes offset, so I suspect there are missing/extra segments or timing inconsistencies. What I need is a tool or workflow that can: Listen to the video/audio track Detect dialogue timing Automatically realign or stretch/squeeze audio to match speech in the video Correct drift issues over long duration files (full movies) Online tools often fail due to file size/length limits, so I’m specifically looking for local software or AI models that can run on a PC . Any suggestions for tools, pipelines, or approaches appreciated. submitted by /u/dotmerlin [link] [留言]

Stepping up to give my first-ever presentation at UbuCon 2026 was a massive milestone, and honestly, it was pretty intimidating. The stakes felt high, especially with the live demo. It was a race against the clock to get everything running, and it only finally came together exactly ten minutes before I went on stage. Talk about a close call. While I am proud of what I delivered, I originally wanted to pack even more into the session. I had planned to showcase a simulated mission, Gazebo visualizations and RViz path simulations. While time caught up with me for the presentation, these features are still actively in the works over at the aeronix project. My goal is to have the entire end-to-end setup completed and ready by the end of the year. I connected with some incredible engineers and industry peers and I am looking forward to building on those conversations for future professional collaborations. This experience proved that the best way to grow is to just put yourself out there. Moving forward, I plan to keep speaking on topics that challenge me. It is the ultimate way to deepen my own technical understanding share what I have learned with the community and grow professionally.

i'm trying to train an ASR model using the LibriSpeech recipe from SpeechBrain (without the language model) on a 100-hour dataset of dialectal Arabic speech. the model architecture uses a Conformer-small encoder and a Transformer decoder, with a total of around 13M parameters. the recipe uses a combination of two loss functions: CTC and KL divergence, specifically: 0.3 * CTC + 0.7 * KLDiv during training, both losses drop significantly during the first few weight updates, but then quickly plateau. the CTC loss gets stuck fluctuating around the 60-80 range, while the KL divergence loss remains around the 60s as well for the rest of training. as a result, the model does not converge properly, and the validation WER stays close to 100%. i’ve already tried several things: adjusting the learning rate, changing the number of warmup steps, modifying the number of epochs, tuning the batch size and reducing the vocabulary size from the default 5000 to 1000. none of these changes seem to help. the training dataset is not publicly available and is weakly labeled. the validation and test sets come from the MGB2 dataset. at this point, i genuinely don’t know what the root cause might be. i’ve experimented with many different approaches, but the model still refuses to converge. has anyone encountered a similar issue where their model gets stuck early in training and never improves? if so, what ended up being the cause or solution? any feedback, suggestions, or ideas would be greatly appreciated. submitted by /u/Sweet-Hamster-4991 [link] [留言]

Every time I sit down to learn something, I find myself trapped in the same cycle. I start with a tutorial. Halfway through, someone says there's a better tutorial. I switch. Then I discover a book that supposedly explains the topic better than the tutorial. Then a YouTube video claims the book is outdated. Then a developer on social media recommends an entirely different resource. Before I know it, I've spent three hours researching how to learn instead of actually learning. Does the information overload stop or will there always be another resource, another course, another book, another video, another roadmap, another expert with a different opinion. The internet has made knowledge abundant, but abundance creates a paradox of choice. One person says to learn JavaScript from documentation, another says build projects immediately, another recommends a paid course, someone else insists that free resources are better. Every recommendation sounds convincing. Every path seems important. The result is paralysis. Instead of moving forward, I keep searching, instead of building I keep comparing, instead of learning I keep consuming. finished teaches more than a hundred bookmarked tutorials. At some point, every learner must accept a difficult truth that the goal is not to find the best resource it is to become better. Those are not the same thing. A person can spend months researching the perfect learning path and never write a meaningful line of code while another person can pick a decent resource, make mistakes, build projects, and improve every day. The second person wins not because they found better information but because they used the information they already had. I've started realizing that learning is a lot like fitness. At some point, reading about exercise becomes a form of avoiding exercise. The same thing happens in programming. Reading about coding becomes a way to avoid coding. Researching becomes a substitute for practice. The search for the perfect resourc

submitted by /u/TheOnlyVibemaster [link] [留言]

submitted by /u/Raman606surrey [link] [留言]

submitted by /u/InvestigatorSoft5764 [link] [留言]

At what point did your memory layer become the least trusted part of your stack? Mine wasn't retrieval. It was realizing I had no idea which memories were still true. submitted by /u/riddlemewhat2 [link] [留言]

The YouTube-to-prestige-horror pipeline is looking very strong.

AI จะครองโลก เป็นคำที่ได้ยินมานาน เท่าที่ผู้เขียนจำความได้ก็มี Judgement Day ยุคหนัง Terminator แต่หากจะจริงจังขนาดโยงเข้าความเป็นจริงก็ยังไม่มีอะไรชัดเจน แต่วันนี้เรามีหลักฐานพิสูจน์ได้จริงแล้ว ด้วยความ Novelty จาก OpenAI ที่สามารถค้นพบความรู้ใหม่ที่ไม่เคยมีมนุษย์ค้นพบมาก่อน หักล้างความเชื่อที่ว่า AI ทำได้เพียงนำสิ่งที่มนุษย์ค้นพบแล้วมาเรียงต่อกัน ในเดือนพฤษภาคม 2026 reasoning model ภายในของ OpenAI ได้ disprove Erdős Planar Unit Distance Problem ซึ่งเป็นปัญหาและข้อคาดการณ์ทาง Combinatorial geometry ที่ Paul Erdős ตั้งไว้ตั้งแต่ปี 1946 โจทย์ระบุว่า เมื่อวางจุด nn n จุดบนระนาบ จำนวนของคู่จุดที่ห่างกันพอดี 1 หน่วยจะมีได้มากที่สุดเท่าใด Erdős แสดงความเป็นไปได้ผ่านการจัดเรียงแบบ grid ว่าได้จำนวนคู่ที่เติบโตเหนือเส้นตรงเพียงเล็กน้อย และตั้งข้อคาดการณ์ว่าไม่มีโครงสร้างใดทำได้ดีกว่านี้อย่างมีนัยสำคัญ ข้อคาดการณ์นี้ได้รับการยอมรับในวงกว้างตลอด 80 ปีที่ผ่านมา และยังไม่มีข้อคาดการณ์ที่ดีกว่านี้ จนกระทั่ง OpenAI ได้เผยแพร่ Chain of Thought (CoT) เรียบเรียงความยาว 125 หน้า ซึ่งบันทึกลำดับการให้เหตุผลของโมเดลไว้ทั้งกระบวนการว่าโมเดลไปถึงคำตอบอย่างไร กรอบของคำตอบ: lower bound กับ upper bound ก่อนเข้ากระบวนการทำงานของโมเดล ขออธิบาย "กรอบ" ของคำตอบของปัญหานี้ก่อน เพราะคำตอบถูกล้อมไว้ด้วย lower bound จำนวนที่สร้างได้จริงแล้ว อย่างน้อยเท่านี้ และ upper bound เพดานที่พิสูจน์แล้วว่าเกินไม่ได้ ด้าน lower bound นั้น Erdős เอง (1946) ใช้การจัดเรียงแบบ grid แสดงว่าสร้างได้ถึง n1+Ω(1/log⁡log⁡n)n^{1+\Omega(1/\log\log n)} n 1 + Ω ( 1/ l o g l o g n ) ซึ่งมากกว่าเส้นตรงเพียงเล็กน้อย และเข้าใกล้ศูนย์เมื่อ nn n ใหญ่ขึ้น ส่วนด้าน upper bound นั้น Erdős พิสูจน์เพดานแรกไว้ที่ O(n3/2)O(n^{3/2}) O ( n 3/2 ) จากวงกลมหนึ่งหน่วยสองวงตัดกันได้ไม่เกินสองจุด โดยต่อมา Spencer–Szemerédi–Trotter (1984) บีบเพดานนี้ลงมาเป็น O(n4/3)O(n^{4/3}) O ( n 4/3 ) ซึ่งเป็น upper bound ที่ดีที่สุดจนถึงปัจจุบัน และยังคงอยู่หลังการค้นพบของ OpenAI model วิธีเก่าของ Erdős: วงกลมรัศมีเลือกมาลากผ่านจุด grid หลายจุดพร้อมกัน ทำให้ระยะซ้ำมีมาก สิ่งที่ Erdős คาดการณ์คือ คำตอบจริงของ Planar Unit Distance Problem ควรอยู่ชิดด้าน lower

The startup has apparently taken down the ads using KC Green's "This is fine" meme.