bilinear_family_forced
The bilinear cost family is forced by the d'Alembert factorization.
plain-language theorem explainer
Any cost functional normalized at unity and multiplicatively consistent with a symmetric quadratic polynomial combiner must have that combiner of bilinear form 2u + 2v + c uv. Recognition Science researchers cite the result to establish that the d'Alembert equation is the unique form forced by consistency. The proof extracts coefficients from the polynomial assumption via normalization at 1 and P-symmetry, then fixes the remaining terms using non-triviality of F.
Claim. Let $F : (0,∞) → ℝ$ satisfy $F(1) = 0$ and let $P : ℝ → ℝ → ℝ$ be a symmetric polynomial of degree at most 2 such that $F(xy) + F(x/y) = P(F(x), F(y))$ for all $x,y > 0$. Assume $F$ is non-trivial and continuous on $(0,∞)$. Then there exists $c ∈ ℝ$ such that $P(u,v) = 2u + 2v + c uv$ for all $u,v$, and if $c=2$ then $P(u,v) = 2u + 2v + 2uv$.
background
The module establishes that the d'Alembert equation is the unique form compatible with multiplicative consistency for cost functionals. IsNormalized encodes the condition $F(1) = 0$, the normalization that treats $F$ as a cost of deviation from unity. HasMultiplicativeConsistency encodes the relation $F(xy) + F(x/y) = P(F(x), F(y))$ where $P$ is required to be symmetric and quadratic. The local theoretical setting is that symmetry, normalization, and polynomial consistency together force the bilinear family, closing the transcendental argument for the axiom bundle without presupposing the specific RCL coefficients.
proof idea
The proof first obtains symmetry of $F$ from symmetry of $P$ via the consistency relation. It applies the normalization lemma to constrain $P(0, F(y)) = 2 F(y)$. Expanding the given polynomial form and substituting the consistency equation at $(1,1)$ forces the constant term to vanish. Symmetry of $P$ then equates the linear coefficients and the quadratic coefficients. Non-triviality supplies a point where $F(y) ≠ 0$, which forces the remaining coefficients to the bilinear values $c=2$ and zero quadratic term.
why it matters
This theorem supplies the central step showing that the RCL form is inevitable once consistency is required to hold with a symmetric quadratic polynomial. It feeds directly into axiom_bundle_necessary, which concludes that the full axiom bundle (normalization, RCL, calibration) is transcendentally necessary, and into consistency_forces_RCL_form_is_theorem, which fixes the canonical scale $c=2$. In the framework it completes the argument that A2 is not an arbitrary choice but the unique polynomial solution compatible with the cost axioms.
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papers checked against this theorem (showing 30 of 150)
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Controller flags tool faults by mismatching cylinder position and pressure changes
"determine an expected pressure change based on the position change; determine an expected position change based on the pressure change; identify a fault in response to determining the position change is not within a first threshold range of the expected position change, or determining the pressure change is not within a second threshold range of the expected pressure change"
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Greenhouse solar panels lock to sensor-chosen angles for crop growth
"a controller; and a plurality of sensors, each of the plurality of sensors configured to monitor at least one of air temperature, leaf temperature, root temperature, irradiance, humidity, and carbon dioxide concentration"
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Reverse engineering AI internals could prevent catastrophic failures
"Mechanistic interpretability: reverse engineering the computational mechanisms and representations learned by neural networks into human-understandable algorithms and concepts to provide a granular, causal understanding."
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TabICL scales in-context learning to 500K-row tables
"Across 200 classification datasets from the TALENT benchmark, TabICL is on par with TabPFNv2 while being systematically faster (up to 10 times)"
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NMSSM explains CMS dilepton endpoint via new neutralino cascades
"NMSSM scenarios are investigated to explain an excess in the opposite-sign dilepton mass distribution..."
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Bi-event subtraction tags W decays and fixes dark matter density in non-universal superGRA
"We develop a bi-event subtraction technique to remove a huge combinatorial background to identify W -> jj decays. ... With the model parameters, we find that the dark matter content of the universe can be determined in agreement with existing experimental results."
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Only select heads need full caches for long-context LLMs
"DuoAttention uses a lightweight, optimization-based algorithm with synthetic data to identify retrieval heads accurately."
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Variational bottleneck improves neural net generalization
"I(Z,Y;θ)−βI(Z,X;θ)"
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Causal reasoning lifts planning accuracy in rare driving cases
"AR1 achieves up to a 12% improvement in planning accuracy... RL post-training improves reasoning quality by 45% and reasoning-action consistency by 37%"
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New benchmark shows top LLM judges barely above random on hard tasks
"JudgeBench poses a significantly greater challenge than previous benchmarks, with many strong models (e.g., GPT-4o) performing just slightly better than random guessing."
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Fine-tuning on reward-ranked samples aligns generative models
"Our studies show that RAFT can effectively improve the model performance in both reward learning and other automated metrics in both large language models and diffusion models."
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Data shift trains models to fill text gaps without losing forward skill
"we provide extensive evidence that training models with a large fraction of data transformed in this way does not harm the original left-to-right generative capability, as measured by perplexity and sampling evaluations across a wide range of scales"
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Speculative decoding speeds up transformers 2-3x
"Our method can accelerate existing off-the-shelf models without retraining or architecture changes. We demonstrate it on T5-XXL and show a 2X-3X acceleration compared to the standard T5X implementation, with identical outputs."
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RL skill library raises agent success rate by 8.9 percent
"Experimental results on AppWorld demonstrate that SAGE... achieves 8.9% higher Scenario Goal Completion while requiring 26% fewer interaction steps and generating 59% fewer tokens"
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GPTScore: Evaluate as You Desire
"This nature helps us overcome several long-standing challenges in text evaluation--how to achieve customized, multi-faceted evaluation without the need for annotated samples."
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Response order tricks LLM evaluators into skewed rankings
"the quality ranking of candidate responses can be easily hacked by simply altering their order of appearance in the context"
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Fair principles over true morals for AI alignment
"A principle-based approach to AI alignment, which combines these elements in a systematic way, has considerable advantages in this context"
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Simple MLP matches planners on nuScenes with 20% lower L2 error
"we design an MLP-based method that takes raw sensor data (e.g., past trajectory, velocity, etc.) as input and directly outputs the future trajectory of the ego vehicle, without using any perception or prediction information such as camera images or LiDAR."
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ORION: A Holistic End-to-End Autonomous Driving Framework by Vision-Language Instructed Action Generation
"ORION further aligns the reasoning space and the action space to implement a unified E2E optimization for both visual question-answering (VQA) and planning tasks. Our method achieves an impressive closed-loop performance of 77.74 Driving Score (DS) and 54.62% Success Rate (SR)"
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Hot-pressed cotton base and cover seal pet filter material in chamber
"A pet water filter cartridge, comprising a water filter cartridge carrier and a filter material (1), wherein the water filter cartridge carrier is composed of a non-woven cotton hot pressed base (2) and a non-woven cotton cover (3); the base (2) is provided with an accommodation chamber (4), the accommodation chamber (4) is provided with the filter material (1), the non-woven cotton cover (3) is sealed at an opening of the accommodation chamber (4)."
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LLM agents complete full research cycles from idea to SOTA code
"The generated machine learning code is able to achieve state-of-the-art performance compared to existing methods"
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Distillation reduces denoising to one step with GAN-level quality
"Our Denoising Student generates high quality samples comparable to GANs on the CIFAR-10 and CelebA datasets, without adversarial training"
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No current AI meets consciousness indicators from brain science
"From these theories we derive 'indicator properties' of consciousness, elucidated in computational terms that allow us to assess AI systems for these properties... Our analysis suggests that no current AI systems are conscious, but also suggests that there are no obvious technical barriers to building AI systems which satisfy these indicators."
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RL post-training reaches SOTA video grounding with 2.5K examples
"GRPO training. The LVLM F (·) takes the video frames x1, . . . , xt and the language query q as input and generates G candidate responses o1, . . . , oG"
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EvoSkill evolves agent skills to boost accuracy 7-12% without retraining
"EvoSkill analyzes execution failures, proposes new skills or edits to existing ones, and materializes them into structured, reusable skill folders. A Pareto frontier of agent programs governs selection, retaining only skills that improve held-out validation performance while the underlying model remains frozen."
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Few examples unlock strong math reasoning in LLMs
"LIMO Hypothesis: In foundation models where domain knowledge has been comprehensively encoded during pre-training, sophisticated reasoning can emerge through minimal but strategically designed demonstrations of cognitive processes."
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Diffusion LLMs reach 1109 tokens per second on H100
"Mercury models are parameterized via the Transformer architecture and trained to predict multiple tokens in parallel... generating tokens in parallel in a coarse-to-fine manner"
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Text-only training makes MLLMs universal embedders
"We propose a single modality training approach for E5-V, where the model is trained exclusively on text pairs. This method demonstrates significant improvements over traditional multimodal training on image-text pairs, while reducing training costs by approximately 95%."
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RL lets 2B vision model beat GPT-4.1 on chart tool tasks
"V-ToolRL enables LVLMs to autonomously discover optimal tool-usage strategies by directly optimizing for task success using feedback from tool interactions."
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Optimizer seeks flat loss regions to boost generalization
"SAM seeks parameters that lie in neighborhoods having uniformly low loss; this formulation results in a min-max optimization problem"