@arXiv_condmatmtrlsci_bot@mastoxiv.page2025-07-18 09:22:12
Suppression of Thermal Conductivity via Singlet-Dominated Scattering in TmFeO$_3$
M. L. McLanahan, D. Lederman, A. P. Ramirez
https://arxiv.org/abs/2507.12608
@arXiv_condmatmtrlsci_bot@mastoxiv.page2025-07-18 09:22:12
@arXiv_csCV_bot@mastoxiv.page2025-07-16 10:37:01
CATVis: Context-Aware Thought Visualization
Tariq Mehmood, Hamza Ahmad, Muhammad Haroon Shakeel, Murtaza Taj
https://arxiv.org/abs/2507.11522 https://
@unchartedworlds@scicomm.xyz2025-07-17 19:39:08
Content warning: covid in the UK - stats & map
Useful thread from @… with latest UK covid positivity results (from last week). Going up a bit - more noticeably in certain areas.
For context, "positivity rate" isn't "how many people have covid overall": it's "when we bothered actually doing tests, how many of the tests came back positive".
So for example if you test 30 people, and 3 of the tests came back positive, that's a "10% positivity rate".
Sometimes there are blank places on the map where, if there _was_ any testing that week, they didn't bother sending it in.
#covid #UK #stats
@arXiv_csCL_bot@mastoxiv.page2025-06-16 13:51:16
Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[1/3]:
FlashBack:Efficient Retrieval-Augmented Language Modeling for Long Context Inference
@arXiv_csSE_bot@mastoxiv.page2025-06-17 11:02:25
Adopting Use Case Descriptions for Requirements Specification: an Industrial Case Study
Julian Frattini, Anja Frattini
https://arxiv.org/abs/2506.13303 htt…
@usul@piaille.fr2025-06-11 11:31:32
Focus and Context and LLMs | Taras' Blog on AI, Perf, Hacks
#AI
@arXiv_astrophSR_bot@mastoxiv.page2025-06-17 11:13:21
Collision-induced mass loss and mass gain on an extremely massive star. An analytical approach and a static proto-globular cluster test-case
Laura Ram\'irez-Galeano, Corinne Charbonnel, Tassos Fragos, Zouba\"ir Tazakkati, Jaime Roman-Garza, Mark Gieles
https://arxiv.org/abs/2506.12132
@arXiv_mathGR_bot@mastoxiv.page2025-07-18 08:39:02
On finite extensions of lamplighter groups
Corentin Bodart
https://arxiv.org/abs/2507.13203 https://arxiv.org/pdf/2507.13203
@michabbb@social.vivaldi.net2025-08-13 09:46:40
🤖 Context-Aware Agents
Build agents maintaining context across hundreds of tool calls and multi-step workflows with complete #API documentation, tool definitions, and interaction histories without losing coherence.
💰 Pricing Structure
Standard rates: Prompts ≤200K tokens ($3/MTok input, $15/MTok output)
Extended context: Prompts >200K tokens ($6/MTok input, $22.50/MTok out…
@unixorn@hachyderm.io2025-08-16 12:47:22
OH on slack
User Story
As Overloaded Olivia, the backend engineer,
I want clear, actionable requirements with business context, so that I can implement the correct solution without burning half a day in meetings or wild guessing.
Acceptance Criteria
1. The story includes functional requirements (not just vibes).
2. Success/failure states are defined happy path edge cases).
3. Any dependencies or blockers are identified.
Bonus: PM/Designer reviewed this and it’s not just a draft in disguise.
#swe #sre #devops #devoops @… @…
@Techmeme@techhub.social2025-06-23 20:45:44
Salesforce launches Agentforce 3 with an observability tool called Command Center and MCP support, and says 8,000 customers have signed up to deploy Agentforce (Larry Dignan/Constellation Research)
https://www.constellationr.com/blog-news/i
@arXiv_qbiobm_bot@mastoxiv.page2025-07-14 08:15:42
Unavailability of experimental 3D structural data on protein folding dynamics and necessity for a new generation of structure prediction methods in this context
Aydin Wells, Khalique Newaz, Jennifer Morones, Jianlin Cheng, Tijana Milenkovi\'c
https://arxiv.org/abs/2507.08188
@arXiv_astrophEP_bot@mastoxiv.page2025-08-15 08:05:32
A constraint on the density of Jupiter's moon Thebe from primordial dynamics
Ian R. Brunton, Konstantin Batygin
https://arxiv.org/abs/2508.10109 https://
@arXiv_mathGT_bot@mastoxiv.page2025-06-10 08:43:52
On symmetry and exterior problems of knotted handlebodies
Yuya Koda, Makoto Ozawa, Yi-Sheng Wang
https://arxiv.org/abs/2506.06713 https://
@arXiv_astrophGA_bot@mastoxiv.page2025-08-14 09:07:32
The Interstellar Medium in IZw18 seen with JWST/MIRI: I. Highly Ionized Gas
L. K. Hunt, A. Aloisi, M. G. Navarro, R. J. Rickards Vaught, B. T. Draine, A. Adamo, F. Annibali, D. Calzetti, S. Hernandez, B. L. James, M. Mingozzi, R. Schneider, M. Tosi, B. Brandl, M. G. del Valle-Espinosa, F. Donnan, A. S. Hirschauer, M. Meixner, D. Rigopoulou, C. T. Richardson, J. M. Levanti, A. R. Basu-Zych
@arXiv_qbioQM_bot@mastoxiv.page2025-08-13 09:02:32
An Interactive Platform for Unified Assessment of Drug-Drug Interactions Using Descriptive and Pharmacokinetic Data
Nadezhda Diadkina
https://arxiv.org/abs/2508.08351 https://…
@arXiv_hepth_bot@mastoxiv.page2025-06-10 17:54:50
This https://arxiv.org/abs/2412.00852 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hept…
@arXiv_csCR_bot@mastoxiv.page2025-07-10 09:16:21
Bridging AI and Software Security: A Comparative Vulnerability Assessment of LLM Agent Deployment Paradigms
Tarek Gasmi, Ramzi Guesmi, Ines Belhadj, Jihene Bennaceur
https://arxiv.org/abs/2507.06323
@arXiv_mathDG_bot@mastoxiv.page2025-07-31 08:56:11
Hypersurfaces of six-dimensional nearly K\"ahler manifolds
Mateo Anarella, Marie D'haene
https://arxiv.org/abs/2507.22526 https://arxiv.org/pdf/25…
@rperezrosario@mastodon.social2025-07-19 01:09:31
Software Engineer Will Larson unpacks a lot in this July 2025 post. Key takeaway use cases of agentic AI include:
1. Using an LLM to evaluate a context window and get a result.
2. Using an LLM to suggest tools relevant to the context window, then enrich it with the tool’s response.
3. Managing flow control for tool usage.
4. Doing anything software can do to build better context windows to pass on to LLMs.
"What can agents actually do?"
@jorgecandeias@mastodon.social2025-06-03 17:15:14
Olha, @… . Como eu dizia ontem.
https://bsky.brid.gy/r/https://bsky.ap
@arXiv_physicsgenph_bot@mastoxiv.page2025-06-04 07:46:33
The Galactic Pizza: Flat Rotation Curves in the Context of Cosmological Time-Energy Coupling
Artur Novais, Andr\'e L. B. Ribeiro
https://arxiv.org/abs/2506.02045
@arXiv_csAI_bot@mastoxiv.page2025-07-02 14:25:35
Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[3/5]:
- Towards Large-Scale In-Context Reinforcement Learning by Meta-Training in Randomized Worlds
Wang, Shao, Zhang, Yu, Liu, Ding, Cao, Kang, Wang
@tiotasram@kolektiva.social2025-06-21 02:34:13
Why AI can't possibly make you more productive; long
#AI and "productivity", some thoughts:
Edit: fixed some typos.
Productivity is a concept that isn't entirely meaningless outside the context of capitalism, but it's a concept that is heavily inflected in a capitalist context. In many uses today it effectively means "how much you can satisfy and/or exceed your boss' expectations." This is not really what it should mean: even in an anarchist utopia, people would care about things like how many shirts they can produce in a week, although in an "I'd like to voluntarily help more people" way rather than an "I need to meet this quota to earn my survival" way. But let's roll with this definition for a second, because it's almost certainly what your boss means when they say "productivity", and understanding that word in a different (even if truer) sense is therefore inherently dangerous.
Accepting "productivity" to mean "satisfying your boss' expectations," I will now claim: the use of generative AI cannot increase your productivity.
Before I dive in, it's imperative to note that the big generative models which most people think of as constituting "AI" today are evil. They are 1: pouring fuel on our burning planet, 2: psychologically strip-mining a class of data laborers who are exploited for their precarity, 3: enclosing, exploiting, and polluting the digital commons, and 4: stealing labor from broad classes of people many of whom are otherwise glad to give that labor away for free provided they get a simple acknowledgement in return. Any of these four "ethical issues" should be enough *alone* to cause everyone to simply not use the technology. These ethical issues are the reason that I do not use generative AI right now, except for in extremely extenuating circumstances. These issues are also convincing for a wide range of people I talk to, from experts to those with no computer science background. So before I launch into a critique of the effectiveness of generative AI, I want to emphasize that such a critique should be entirely unnecessary.
But back to my thesis: generative AI cannot increase your productivity, where "productivity" has been defined as "how much you can satisfy and/or exceed your boss' expectations."
Why? In fact, what the fuck? Every AI booster I've met has claimed the opposite. They've given me personal examples of time saved by using generative AI. Some of them even truly believe this. Sometimes I even believe they saved time without horribly compromising on quality (and often, your boss doesn't care about quality anyways if the lack of quality is hard to measure of doesn't seem likely to impact short-term sales/feedback/revenue). So if generative AI genuinely lets you write more emails in a shorter period of time, or close more tickets, or something else along these lines, how can I say it isn't increasing your ability to meet your boss' expectations?
The problem is simple: your boss' expectations are not a fixed target. Never have been. In virtue of being someone who oversees and pays wages to others under capitalism, your boss' game has always been: pay you less than the worth of your labor, so that they can accumulate profit and thus more capital to remain in charge instead of being forced into working for a wage themselves. Sure, there are layers of management caught in between who aren't fully in this mode, but they are irrelevant to this analysis. It matters not how much you please your manager if your CEO thinks your work is not worth the wages you are being paid. And using AI actively lowers the value of your work relative to your wages.
Why do I say that? It's actually true in several ways. The most obvious: using generative AI lowers the quality of your work, because the work it produces is shot through with errors, and when your job is reduced to proofreading slop, you are bound to tire a bit, relax your diligence, and let some mistakes through. More than you would have if you are actually doing and taking pride in the work. Examples are innumerable and frequent, from journalists to lawyers to programmers, and we laugh at them "haha how stupid to not check whether the books the AI reviewed for you actually existed!" but on a deeper level if we're honest we know we'd eventually make the same mistake ourselves (bonus game: spot the swipe-typing typos I missed in this post; I'm sure there will be some).
But using generative AI also lowers the value of your work in another much more frightening way: in this era of hype, it demonstrates to your boss that you could be replaced by AI. The more you use it, and no matter how much you can see that your human skills are really necessary to correct its mistakes, the more it appears to your boss that they should hire the AI instead of you. Or perhaps retain 10% of the people in roles like yours to manage the AI doing the other 90% of the work. Paradoxically, the *more* you get done in terms of raw output using generative AI, the more it looks to your boss as if there's an opportunity to get enough work done with even fewer expensive humans. Of course, the decision to fire you and lean more heavily into AI isn't really a good one for long-term profits and success, but the modern boss did not get where they are by considering long-term profits. By using AI, you are merely demonstrating your redundancy, and the more you get done with it, the more redundant you seem.
In fact, there's even a third dimension to this: by using generative AI, you're also providing its purveyors with invaluable training data that allows them to make it better at replacing you. It's generally quite shitty right now, but the more use it gets by competent & clever people, the better it can become at the tasks those specific people use it for. Using the currently-popular algorithm family, there are limits to this; I'm not saying it will eventually transcend the mediocrity it's entwined with. But it can absolutely go from underwhelmingly mediocre to almost-reasonably mediocre with the right training data, and data from prompting sessions is both rarer and more useful than the base datasets it's built on.
For all of these reasons, using generative AI in your job is a mistake that will likely lead to your future unemployment. To reiterate, you should already not be using it because it is evil and causes specific and inexcusable harms, but in case like so many you just don't care about those harms, I've just explained to you why for entirely selfish reasons you should not use it.
If you're in a position where your boss is forcing you to use it, my condolences. I suggest leaning into its failures instead of trying to get the most out of it, and as much as possible, showing your boss very clearly how it wastes your time and makes things slower. Also, point out the dangers of legal liability for its mistakes, and make sure your boss is aware of the degree to which any of your AI-eager coworkers are producing low-quality work that harms organizational goals.
Also, if you've read this far and aren't yet of an anarchist mindset, I encourage you to think about the implications of firing 75% of (at least the white-collar) workforce in order to make more profit while fueling the climate crisis and in most cases also propping up dictatorial figureheads in government. When *either* the AI bubble bursts *or* if the techbros get to live out the beginnings of their worker-replacement fantasies, there are going to be an unimaginable number of economically desperate people living in increasingly expensive times. I'm the kind of optimist who thinks that the resulting social crucible, though perhaps through terrible violence, will lead to deep social changes that effectively unseat from power the ultra-rich that continue to drag us all down this destructive path, and I think its worth some thinking now about what you might want the succeeding stable social configuration to look like so you can advocate towards that during points of malleability.
As others have said more eloquently, generative AI *should* be a technology that makes human lives on average easier, and it would be were it developed & controlled by humanists. The only reason that it's not, is that it's developed and controlled by terrible greedy people who use their unfairly hoarded wealth to immiserate the rest of us in order to maintain their dominance. In the long run, for our very survival, we need to depose them, and I look forward to what the term "generative AI" will mean after that finally happens.
@arXiv_csLG_bot@mastoxiv.page2025-07-31 13:34:56
Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/4]:
- Provable Low-Frequency Bias of In-Context Learning of Representations
Yongyi Yang, Hidenori Tanaka, Wei Hu
…
@berlinbuzzwords@floss.social2025-05-29 11:00:17
Modern applications require search capabilities that go beyond basic text matching. They must be fast, accurate, personalised and context-aware. At this year's Berlin Buzzwords, Saurabh Singh will demonstrate how OpenSearch’s latest AI/ML enhancements and engine improvements enable organisations to build intelligent, scalable search experiences that meet these evolving needs.
Learn more:
@arXiv_physicsclassph_bot@mastoxiv.page2025-06-03 07:45:27
Does Newtonian dynamics need Euclidean space?
Alain Albouy
https://arxiv.org/abs/2506.00086 https://arxiv.org/pdf/2506.00086
@michabbb@social.vivaldi.net2025-07-30 19:53:57
💰 Supports multiple dimensions and quantization options - binary 512d version outperforms OpenAI-v3-large while reducing vector database costs by 99.48%
🔍 Processes entire documents in single pass to generate chunk embeddings enriched with document-level context
🎯 Less sensitive to chunking strategies compared to traditional context-agnostic embedding models
@arXiv_hepth_bot@mastoxiv.page2025-07-25 10:01:32
Open strings in type IIB AdS$_3$ flux vacua
\'Alvaro Arboleya, Adolfo Guarino, Matteo Morittu, Giuseppe Sudano
https://arxiv.org/abs/2507.18529 https://
@arXiv_csCL_bot@mastoxiv.page2025-08-08 10:04:32
H-Net : Hierarchical Dynamic Chunking for Tokenizer-Free Language Modelling in Morphologically-Rich Languages
Mehrdad Zakershahrak, Samira Ghodratnama
https://arxiv.org/abs/2508.05628
@arXiv_astrophHE_bot@mastoxiv.page2025-06-03 17:01:58
This https://arxiv.org/abs/2409.19441 has been replaced.
link: https://scholar.google.com/scholar?q=a
@arXiv_mathDG_bot@mastoxiv.page2025-06-10 17:10:09
This https://arxiv.org/abs/2407.03127 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…
@arXiv_mathAG_bot@mastoxiv.page2025-05-30 09:58:23
This https://arxiv.org/abs/2208.14711 has been replaced.
link: https://scholar.google.com/scholar?q=a
@arXiv_nlincd_bot@mastoxiv.page2025-05-28 10:25:09
This https://arxiv.org/abs/2505.05852 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_nli…
@arXiv_grqc_bot@mastoxiv.page2025-07-30 09:28:21
Lunar Reference Timescale
Adrien Bourgoin (LTE, Observatoire de Paris, Universit\'e PSL, CNRS, Sorbonne Universit\'e, LNE, Paris, France), Pascale Defraigne (Royal Observatory of Belgium, Brussels, Belgium), Fr\'ed\'eric Meynadier (Time Department, BIPM, Pavillon de Breteuil, S\`evres, France)
https://arxiv.org/abs/2507.215…
@michabbb@social.vivaldi.net2025-07-30 19:53:57
#VoyageAI introduces voyage-context-3, a contextualized chunk #embedding #llm that captures both chunk details and full document context 🔍
@arXiv_csCL_bot@mastoxiv.page2025-08-06 14:26:50
Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[3/4]:
- LaMPE: Length-aware Multi-grained Positional Encoding for Adaptive Long-context Scaling Without T...
Sikui Zhang, Guangze Gao, Ziyun Gan, Chunfeng Yuan, Zefeng Lin, Houwen Peng, Bing Li, Weiming Hu
@arXiv_astrophGA_bot@mastoxiv.page2025-06-03 07:45:53
Millimeter-wave observations of Euclid Deep Field South using the South Pole Telescope: A data release of temperature maps and catalogs
M. Archipley, A. Hryciuk, L. E. Bleem, K. Kornoelje, M. Klein, A. J. Anderson, B. Ansarinejad, M. Aravena, L. Balkenhol, P. S. Barry, K. Benabed, A. N. Bender, B. A. Benson, F. Bianchini, S. Bocquet, F. R. Bouchet, E. Camphuis, M. G. Campitiello, J. E. Carlstrom, J. Cathey, C. L. Chang, S. C. Chapman, P. Chaubal, P. M. Chichura, A. Chokshi, T. -L. Chou…
@rperezrosario@mastodon.social2025-05-23 21:17:28
Azure Database for PostgreSQL Blog author and Microsoft employee "JoshMSFT" shares details on Microsoft's new PostgreSQL extension for Visual Studio Code.
Feature set includes:
1. Schema Visualization
2. Database aware GitHub Copilot
3. Database Explorer
4. Query History
5. Query Editing with Context-aware IntelliSense
"Announcing a new IDE for PostgreSQL in VS Code from Microsoft"
@arXiv_condmatquantgas_bot@mastoxiv.page2025-07-29 08:17:31
Low-energy atomic scattering: s-wave relation between the interaction potential and the phase shift
Francesco Lorenzi, Luca Salansich
https://arxiv.org/abs/2507.20421 https://…
@arXiv_hepph_bot@mastoxiv.page2025-06-19 09:33:48
Thermal corrections to Regge trajectories
R. Cadiz, M. Loewe, R. Zamora
https://arxiv.org/abs/2506.14979 https://arxiv.org/pdf/2506.1…
@arXiv_astrophIM_bot@mastoxiv.page2025-07-22 09:55:30
Electron impact ro-vibrational transitions and dissociative recombination of H2 and HD : Rate coefficients and astrophysical implications
Riyad Hassaine, Emerance Djuissi, Nicolina Pop, Felix Iacob, Michel D. Ep\'ee Ep\'ee, Ousmanou Motapon, Vincenzo Laporta, Razvan Bogdan, Mehdi Ayouz, Mourad Telmini, Carla M. Coppola, Daniele Galli, Janos Zs. Mezei, Ioan F. Schneider
@arXiv_mathCV_bot@mastoxiv.page2025-07-23 08:53:22
Rigidity of proper holomorphic self-mappings of the hexablock
Enchao Bi, Zeinab Shaaban, Guicong Su
https://arxiv.org/abs/2507.16176 https://
@arXiv_mathDG_bot@mastoxiv.page2025-06-05 09:47:46
This https://arxiv.org/abs/2501.01444 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…
@arXiv_csCR_bot@mastoxiv.page2025-06-25 08:23:20
WebGuard :Interpretable Malicious URL Detection via Bidirectional Fusion of HTML Subgraphs and Multi-Scale Convolutional BERT
Ye Tian, Zhang Yumin, Yifan Jia, Jianguo Sun, Yanbin Wang
https://arxiv.org/abs/2506.19356
@arXiv_hepth_bot@mastoxiv.page2025-07-01 08:11:33
Splitting Regions and Shrinking Islands from Higher Point Constraints
Justin Berman, Henriette Elvang, Carolina Figueiredo
https://arxiv.org/abs/2506.22538
@arXiv_qfinGN_bot@mastoxiv.page2025-06-18 10:17:10
Optimal Incentive for Regulated Production
Benhao Du, Thomas Treillard, Francois Wang
https://arxiv.org/abs/2506.14286 https://arxiv.…
@arXiv_csCL_bot@mastoxiv.page2025-07-31 13:22:08
Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[2/3]:
- MiniLongBench: The Low-cost Long Context Understanding Benchmark for Large Language Models
Zhongzhan Huang, Guoming Ling, Shanshan Zhong, Hefeng Wu, Liang Lin
@arXiv_astrophGA_bot@mastoxiv.page2025-06-30 09:55:50
MeerKAT radio continuum imaging of nearby star-forming spirals in the NGC 6221, NGC 3256/3263, and NGC 2434 galaxy groups
J. Saponara, B. S. Koribalski, J. English, P. K. Humire
https://arxiv.org/abs/2506.22382
@arXiv_astrophEP_bot@mastoxiv.page2025-07-21 09:02:50
XUE 10. The CO$_2$-rich terrestrial planet-forming region of an externally irradiated Herbig disk
Jenny Frediani, Arjan Bik, Mar\'ia Claudia Ram\'irez-Tannus, Rens Waters, Konstantin V. Getman, Eric D. Feigelson, Bayron Portilla-Revelo, Beno\^it Tabone, Thomas J. Haworth, Andrew Winter, Thomas Henning, Giulia Perotti, Alexis Brandeker, Germ\'an Chaparro, Pablo Cuartas-Restrepo, Sebasti\'an Hern\'andez, Michael A. Kuhn, Thomas Preibisch, Veronica Roccatagliata, Sierk…
@arXiv_csCL_bot@mastoxiv.page2025-06-26 13:01:39
Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[1/3]:
- A Global Context Mechanism for Sequence Labeling
Conglei Xu, Kun Shen, Hongguang Sun, Yang Xu
@arXiv_astrophGA_bot@mastoxiv.page2025-05-30 10:04:20
This https://arxiv.org/abs/2404.14500 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…
@arXiv_csCL_bot@mastoxiv.page2025-06-25 13:25:14
Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[1/3]:
- Impact of Visual Context on Noisy Multimodal NMT: An Empirical Study for English to Indian Languages
Baban Gain, Dibyanayan Bandyopadhyay, Samrat Mukherjee, Chandranath Adak, Asif Ekbal
@unchartedworlds@scicomm.xyz2025-06-20 16:01:59
BBC bias on Israel
From Jonathan Cook. (Substack link, sorry.)
"In a confrontation with BBC news chief Richard Burgess, journalist Peter Oborne sets out six ways the state broadcaster has wilfully misled audiences on Israel's destruction of Gaza"
For example,
"... the distinguished Israeli historian Avi Shlaim lives in the UK and teaches at Oxford University. ... Shlaim is both knowledgeable about the history of Israeli colonisation of Palestine and truly independent. He is in a position to dispassionately provide the context BBC audiences need to make judgments about what is going on and who is responsible for it.
"And yet extraordinarily, Shlaim has never been invited on by the BBC. ... He is one of the prominent Israelis we are never allowed to hear from, because they are likely to make more credible and mainstream a narrative the BBC wishes to present as fringe, loopy and antisemitic."
Plus some media analysis stats such as:
"The BBC mentioned “occupation” – the essential context for understanding the relationship between Israel and Palestinians – only 14 times in news articles when providing context to the events of 7 October 2023. That amounted to 0.3% of articles. Additional context – decades of Israeli apartheid rule and Israel’s 17-year blockade of Gaza — were entirely missing from the coverage."
#Israel #Palestine #history #Gaza #media #BBC #JonathanCook
@tiotasram@kolektiva.social2025-07-31 16:25:48
LLM coding is the opposite of DRY
An important principle in software engineering is DRY: Don't Repeat Yourself. We recognize that having the same code copied in more than one place is bad for several reasons:
1. It makes the entire codebase harder to read.
2. It increases maintenance burden, since any problems in the duplicated code need to be solved in more than one place.
3. Because it becomes possible for the copies to drift apart if changes to one aren't transferred to the other (maybe the person making the change has forgotten there was a copy) it makes the code more error-prone and harder to debug.
All modern programming languages make it almost entirely unnecessary to repeat code: we can move the repeated code into a "function" or "module" and then reference it from all the different places it's needed. At a larger scale, someone might write an open-source "library" of such functions or modules and instead of re-implementing that functionality ourselves, we can use their code, with an acknowledgement. Using another person's library this way is complicated, because now you're dependent on them: if they stop maintaining it or introduce bugs, you've inherited a problem, but still, you could always copy their project and maintain your own version, and it would be not much more work than if you had implemented stuff yourself from the start. It's a little more complicated than this, but the basic principle holds, and it's a foundational one for software development in general and the open-source movement in particular. The network of "citations" as open-source software builds on other open-source software and people contribute patches to each others' projects is a lot of what makes the movement into a community, and it can lead to collaborations that drive further development. So the DRY principle is important at both small and large scales.
Unfortunately, the current crop of hyped-up LLM coding systems from the big players are antithetical to DRY at all scales:
- At the library scale, they train on open source software but then (with some unknown frequency) replicate parts of it line-for-line *without* any citation [1]. The person who was using the LLM has no way of knowing that this happened, or even any way to check for it. In theory the LLM company could build a system for this, but it's not likely to be profitable unless the courts actually start punishing these license violations, which doesn't seem likely based on results so far and the difficulty of finding out that the violations are happening. By creating these copies (and also mash-ups, along with lots of less-problematic stuff), the LLM users (enabled and encouraged by the LLM-peddlers) are directly undermining the DRY principle. If we see what the big AI companies claim to want, which is a massive shift towards machine-authored code, DRY at the library scale will effectively be dead, with each new project simply re-implementing the functionality it needs instead of every using a library. This might seem to have some upside, since dependency hell is a thing, but the downside in terms of comprehensibility and therefore maintainability, correctness, and security will be massive. The eventual lack of new high-quality DRY-respecting code to train the models on will only make this problem worse.
- At the module & function level, AI is probably prone to re-writing rather than re-using the functions or needs, especially with a workflow where a human prompts it for many independent completions. This part I don't have direct evidence for, since I don't use LLM coding models myself except in very specific circumstances because it's not generally ethical to do so. I do know that when it tries to call existing functions, it often guesses incorrectly about the parameters they need, which I'm sure is a headache and source of bugs for the vibe coders out there. An AI could be designed to take more context into account and use existing lookup tools to get accurate function signatures and use them when generating function calls, but even though that would probably significantly improve output quality, I suspect it's the kind of thing that would be seen as too-baroque and thus not a priority. Would love to hear I'm wrong about any of this, but I suspect the consequences are that any medium-or-larger sized codebase written with LLM tools will have significant bloat from duplicate functionality, and will have places where better use of existing libraries would have made the code simpler. At a fundamental level, a principle like DRY is not something that current LLM training techniques are able to learn, and while they can imitate it from their training sets to some degree when asked for large amounts of code, when prompted for many smaller chunks, they're asymptotically likely to violate it.
I think this is an important critique in part because it cuts against the argument that "LLMs are the modern compliers, if you reject them you're just like the people who wanted to keep hand-writing assembly code, and you'll be just as obsolete." Compilers actually represented a great win for abstraction, encapsulation, and DRY in general, and they supported and are integral to open source development, whereas LLMs are set to do the opposite.
[1] to see what this looks like in action in prose, see the example on page 30 of the NYTimes copyright complaint against OpenAI (#AI #GenAI #LLMs #VibeCoding
@arXiv_csCL_bot@mastoxiv.page2025-06-19 08:13:04
SciVer: Evaluating Foundation Models for Multimodal Scientific Claim Verification
Chengye Wang, Yifei Shen, Zexi Kuang, Arman Cohan, Yilun Zhao
https://arxiv.org/abs/2506.15569
@arXiv_astrophGA_bot@mastoxiv.page2025-07-22 09:33:50
Dwarf-Dwarf interactions and their influence on star formation : Insights from post-merger galaxies
Rakshit Chauhan, Smitha Subramanian, Deepak A. Kudari, S. Amrutha, Mousumi Das
https://arxiv.org/abs/2507.14695
@arXiv_csCL_bot@mastoxiv.page2025-06-27 09:56:19
Domain Knowledge-Enhanced LLMs for Fraud and Concept Drift Detection
Ali \c{S}enol, Garima Agrawal, Huan Liu
https://arxiv.org/abs/2506.21443 https://arxiv.org/pdf/2506.21443 https://arxiv.org/html/2506.21443
arXiv:2506.21443v1 Announce Type: new
Abstract: Detecting deceptive conversations on dynamic platforms is increasingly difficult due to evolving language patterns and Concept Drift (CD)\-i.e., semantic or topical shifts that alter the context or intent of interactions over time. These shifts can obscure malicious intent or mimic normal dialogue, making accurate classification challenging. While Large Language Models (LLMs) show strong performance in natural language tasks, they often struggle with contextual ambiguity and hallucinations in risk\-sensitive scenarios. To address these challenges, we present a Domain Knowledge (DK)\-Enhanced LLM framework that integrates pretrained LLMs with structured, task\-specific insights to perform fraud and concept drift detection. The proposed architecture consists of three main components: (1) a DK\-LLM module to detect fake or deceptive conversations; (2) a drift detection unit (OCDD) to determine whether a semantic shift has occurred; and (3) a second DK\-LLM module to classify the drift as either benign or fraudulent. We first validate the value of domain knowledge using a fake review dataset and then apply our full framework to SEConvo, a multiturn dialogue dataset that includes various types of fraud and spam attacks. Results show that our system detects fake conversations with high accuracy and effectively classifies the nature of drift. Guided by structured prompts, the LLaMA\-based implementation achieves 98\% classification accuracy. Comparative studies against zero\-shot baselines demonstrate that incorporating domain knowledge and drift awareness significantly improves performance, interpretability, and robustness in high\-stakes NLP applications.
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