Instead of node --edge--> node, every relationship is a first-class document with its own vector, called a BaryEdge. Stack pairs of BaryEdges recursively and you get "MetaBary" triads that surface structural bridges between concepts that live nowhere near each other in embedding space. Running locally on MongoDB Community + mongot + nomic-embed-text over the full English Wiktionary (6.6M docs). MCP server is live if you want to poke at it. Preprint + benchmark CSVs available in comments

The problem I was chasing

Flat vector search treats a relationship as a byproduct of two points being close. That throws away information. Two papers can describe the same underlying phenomenon (a flyby anomaly in orbital mechanics, an anomalous residual in stellar dynamics) without ever citing each other and without their embeddings landing anywhere near each other. Nothing in standard RAG surfaces that connection.

What I did instead

Every relationship gets embedded too:

bary_vector = normalize(q·v(CM1) + q·v(CM2) + (1−q)·v(type))

q is connection quality, v(type) is a contextual embedding of what kind of relationship it is. This BaryEdge is now a retrievable document in its own right — not metadata on an edge.

Then it recurses: two BaryEdges at the same level get bridged by a third one level below, forming a MetaBary triad. Do that repeatedly and you climb an abstraction triads hierarchy built entirely from algebra — zero additional embedding calls above the base level. It's a forest (every node has at most one parent), so traversal to root is a single $graphLookup, no cycle handling.

Does it actually do anything useful?

Ran it against SimLex-999 and WordSim-353 as a sanity check (not the main claim, just "is the substrate coherent"). Raw cosine similarity barely correlates with human similarity judgments (ρ ≈ −0.04 on SimLex). Structural metrics — how many BaryEdges two words share, how much their relational neighborhoods overlap — correlate at ρ ≈ 0.32–0.53, p < 10⁻¹⁵. So the graph is encoding something cosine alone doesn't.

The part I actually care about is cross-domain bridging. Some probe traces from the live graph:

• octopus neuroscience ↔ distributed sensor networks, bridged by shared structural-motif vocabulary (neuroarchitecture, smartdust)
• collagen folding ↔ linguistic syntax, bridged by etymological + structural motif overlap (plicature / hypotaxis-parataxis)
• grief ↔ depression, not bridged and this is a correctness demonstration, not a missing capability. The DSM-5 added a much-debated "bereavement exclusion" precisely because grief and depression share surface symptoms but are different kinds of state, with different prognosis and treatment
• radioactive decay ↔ obsolete words falling out of use, bridged at a high abstraction level by register-varied decay verbs (collapsed, decayed, declined, disintegrated) — naming a Poisson-process state-loss pattern that both physics and historical linguistics instantiate, with no single word doing the work

That last one is the case flat retrieval structurally cannot produce — there's no embedding axis for "verbs co-occurring with reduction-of-state across unrelated domains."

Stack (all local, all free)

GitHub: in comments

• MongoDB Community Edition + mongot for storage/vector search
• nomic-embed-text, 768-dim
• Python 3.11+
• Full build: ~6.66M documents, 8–14 hrs on a single workstation (8–16GB VRAM)

Try it

MCP server is public on request (SSE transport) — read-only tools for searching the live graph: find_word, semantic_search, edge_info, leaf_nodes, traverse_up, sample_metabary. If you've got an MCP-capable client you can point it at the graph and run your own probe queries in a few minutes.

What I'd actually want feedback on

• Whether the cross-domain bridges hold up to someone who isn't me poking at them — try a probe query on a domain pair you know well and tell me if the bridge is real or if I'm pattern-matching myself into seeing structure that isn't there. Some bridges can be not obvious on the first look but they are actually the most intriguing ones and worth to be dug for the reason they built, so treat them as points of investigation
• Whether this is worth comparing directly against GraphRAG/RAPTOR-style hierarchical retrieval (I haven't done that benchmark yet, and I know that's the first thing this sub will ask)
• Whether anyone's tried something structurally similar and it fell apart at scale for reasons I haven't hit yet

Happy to drop the MCP endpoint on request if there's interest.

I need an NLP pipeline to help me with wordplay. I'm after a tool that scans vocabulary to find words or phrases with double meanings linked to a target theme for joke angles.

To illustrate the mechanism, consider this Jimmy Carr joke:

The first few weeks of joining Weight Watchers: you're just finding your feet.

Here, "finding your feet" can mean two different things. Figuratively, it's about getting used to a new situation. Literally, it's about being able to look down and see your feet. This example leans on a split between figurative and literal meanings. But I'm trying to find any double meanings that could be used in a joke.

If I put in Weight Watchers as the theme, I'd want the system to pull up phrases like "find one's feet". Ideally, the tool would let me import my list of words and phrases. I've got a vocab list of roughly 100k English words and phrases. I ran Wiktionary through large language models and grabbed the terms that most folks are likely to know.

Is there an NLP tool that can spot double meanings?

Also, I'm curious about how you'd go about building it.

Hey everyone,

I'm currently preparing a novel research proposal for a Master's application targeting a top-tier lab. I'm relatively new to advanced NLP/LLMs, specifically long-context handling and test-time scaling, and want to make sure my direction is genuinely novel.

I’m looking to pay a current PhD student or active researcher for a few hours of their time over the next 20 days to help me vet ideas, look for gaps in recent literature, and help structure a strong abstract.

🔬 Areas of Interest:

• Optimizing retrieval/context window limits in long-context LLMs.
• Inference-time compute scaling laws and search policies.
• Multimodal vision-language alignment.

I value your time and am offering a flat consulting payment for a focused brainstorming session and initial review of the abstract layout. If you're interested, please drop me a DM with a brief note on what you're currently researching

I've been rebuilding spaCy's en_core_web_md pipeline from scratch in Rust, compiled to WASM. Tokenizer, POS tagger, dependency parser, lemmatizer, NER, and the 300-dimension word vectors — all of it, running client-side.

The whole thing is a single self-contained HTML file. The model weights and the Rust runtime are baked right in. You can save it, open it on a plane, and it still works — there is no backend call, no API key, no pip install. Nothing ever leaves your machine.

It's not an approximation. I scored it against spaCy's own output on a 1,000-sentence held-out set:

POS tags: 100%

Fine-grained tags: 100%

Lemmas: 100%

Dependency UAS / LAS: 99.9% / 99.8%

NER F1: 1.00

The demo has a live parse meter (watch the tokens/sec tick as you type), a displaCy-style entity + dependency-arc view, word-vector similarity, and document embeddings — all computed locally, in real time.

One honest caveat: it's a ~45 MB file because the entire model is embedded. That's the price of "works with wifi off, forever."

Disclaimer: I built this heavily with AI assistance — figured I'd be upfront about it. The code is real and the parity numbers are measured, but I'm not going to pretend I hand-wrote every line of Rust. Happy to answer questions about how it actually works.

If there's interest, I'll link the repo.

Curious what people think — especially anyone who's tried to ship spaCy somewhere without a Python runtime.

Over the past year, with 8+ papers submitted to ARR, I can confirm that the quality of reviews has dropped significantly, and this is reflected in discussions with colleagues from many universities and labs who share the same experience.

As an NLP community, what do you think we can do to avoid such low-quality reviews further, while also reducing randomness in paper review assignments? There are several reasons: first, inexperienced authors review the paper and do not clearly understand the task or the evaluation criteria; next, experienced authors are assigned to a new topic; and finally, there are problems with the review rubrics. I think ARR currently lacks explicit criteria for paper evaluation, such as TACL/TMLR journals, like: "Does the paper introduce a new Method? benchmark? evaluation framework/tool? Is the related work properly discussed, and are the baselines properly selected? "

I would be interested to hear what others think. What changes could improve the quality of ARR reviews?

So I have a collection of text , basically a description of job responsibilities,and I want to classify them based on their role. Ex- some roles are management heavy while others require more coding. Now first i thought about using jobBert or job2vec for categorisation but the results were not good. The amount of wrong classification was a bit too much. Next I tried topic modelling (tfidf + pca+ k means, embedding +pca + k means , lda ) but it still got a lot of incorrect results. Now, the only option I can see is using some kind of keyword matching( I'm thinking of comparing keywords for both categories and then choosing the one with the majority). I want to use some pre-LLM techniques and as fast as possible. I have been trying to look for some methods but most of the results used LLM one way or another. So I'm looking for an alternative or anything that can improve the results.

It can find idioms, phrasal verbs, prepositional verbs. I have a huge database of those. The engine is rule-based. I'm planning a second AI-layer to resolve difficult cases. I also have thoughts about making a public service so anyone can analyze any text (and turn the result into Anki cards or an Excel sheet). It seems there's no such tool on the internet. It's an interesting project, and it's more like a way to spend my free time, but I'm wondering if it can be useful or even profitable. What are your thoughts?

hi I’m a student with a background in Linguistics that got offered a place in a Master of NLP, I heard though that the job market wasn’t stable and this is making me doubt a lot.

Would you recommend working in that field or not? Is the job market as unstable as I heard? are long term employment possibilities available?

I know this is not the usual talk that you find here but I really needed someone’s "seasoned" opinion. thank you so much.

Hi, everyone! I’m working on a dataset with both English and Roman Urdu reviews. Anyone who has experience with libraries (built-in or custom) that handle this well? Would love some recommendations!

Is it worth attending ACL in San Diego even if I’m not presenting?

For context, I’m an incoming MS student (starting in Fall) and I presented at EACL earlier this year so I’m not totally new to research. I thought it might be useful to build on connections I’ve made and network for internship purposes etc. + I already know I want to get a PhD in NLP.

I’d be able to stay at a friend’s place, but late registration + domestic flight is still a chunk of money for me, so not sure if I should just stay home / attend virtually.

Would really appreciate any advice/opinions!! Thanks

Title.

Hi! I have a biomedical NLP/RAG paper that we plan to submit to a journal. Is it usually okay in this field to upload it to arXiv while it is under review?

Also, does the arXiv version need a generic template, or is it fine to upload it with the journal/preprint LaTeX template?

I know I should check the specific journal policy, but I’m curious about common practice. Thanks!

1. Are LLMs essentially large memorization machines that are trained to learn patterns from massive datasets?
2. Is the math and reasoning they perform just the result of patterns they have picked up during training, which they then use to answer questions?
3. If LLMs are identifying patterns, could they potentially discover patterns that humans have missed?
4. I remember seeing research where an LLM was trained only on data up to around the 1940s, with no access to later discoveries, and was then tested to see whether it could independently rediscover ideas like Einstein’s relativity. Is this a real line of research, and what does it tell us?
5. Could LLMs find meaningful patterns in randomly generated text or data, or would they just impose patterns where none actually exist?
6. Is true randomness possible, or will some kind of pattern always appear when we analyze enough data and Can LLMs help us find that patterns faster.

I've been exploring advanced LLM research directions and I'm looking to collaborate with others working on serious, research level problems in this space.

Areas of interest include reasoning, agentic systems, memory, planning, multimodal models, and evaluation of foundation models.

I'm looking to connect with researchers, engineers, graduate students, and independent builders who are actively working on challenging problems and open to collaboration or additional contributors.

If you're working on something interesting and open to discussion, feel free to DM me.

I'm really interested in music & wanted to explore how I can use it to build something useful for people.....so I would love to hear some ideas from you guys....

I’m looking for datasets from Slack workspaces or similar team communication tools, especially for testing language tech / RAG / agent workflows. Ideally something with channels, threads, multi-person conversations etc. that is scrubbed of PII / sensitive data.

Does anyone know of datasets like this? Or if you maintain a public/synthetic workspace dataset, would you be willing to share?