Visual arguments, often used in advertising or social causes, rely on images
to persuade viewers to do or believe something. Understanding these arguments
requires selective vision: only specific visual stimuli within an image are
relevant to the argument, and relevance can only be understood within the
context of a broader argumentative structure. While visual arguments are
readily appreciated by human audiences, we ask: are today's AI capable of
similar understanding?
We collect and release VisArgs, an annotated corpus designed to make explicit
the (usually implicit) structures underlying visual arguments. VisArgs includes
1,611 images accompanied by three types of textual annotations: 5,112 visual
premises (with region annotations), 5,574 commonsense premises, and reasoning
trees connecting them to a broader argument. We propose three tasks over
VisArgs to probe machine capacity for visual argument understanding:
localization of premises, identification of premises, and deduction of
conclusions. Experiments demonstrate that 1) machines cannot fully identify the
relevant visual cues. The top-performing model, GPT-4-O, achieved an accuracy
of only 78.5%, whereas humans reached 98.0%. All models showed a performance
drop, with an average decrease in accuracy of 19.5%, when the comparison set
was changed from objects outside the image to irrelevant objects within the
image. Furthermore, 2) this limitation is the greatest factor impacting their
performance in understanding visual arguments. Most models improved the most
when given relevant visual premises as additional inputs, compared to other
inputs, for deducing the conclusion of the visual argument.

AI 的视觉论证理解能力受限，当面临视觉论证时，模型的正确性只有 78.5%，而人类的正确性达到了 98.0%。

选择性视觉是视觉推理的挑战：视觉论证理解的基准测试

Selective Vision is the Challenge for Visual Reasoning: A Benchmark for  Visual Argument Understanding

The tension between deduction and induction is perhaps the most fundamental
issue in areas such as philosophy, cognition and artificial intelligence. In an
influential paper, Valiant recognised that the challenge of learning should be
integrated with deduction. In particular, he proposed a semantics to capture
the quality possessed by the output of Probably Approximately Correct (PAC)
learning algorithms when formulated in a logic. Although weaker than classical
entailment, it allows for a powerful model-theoretic framework for answering
queries. In this paper, we provide a new technical foundation to demonstrate
PAC learning with multi-agent epistemic logics. To circumvent the negative
results in the literature on the difficulty of robust learning with the PAC
semantics, we consider so-called implicit learning where we are able to
incorporate observations to the background theory in service of deciding the
entailment of an epistemic query. We prove correctness of the learning
procedure and discuss results on the sample complexity, that is how many
observations we will need to provably assert that the query is entailed given a
user-specified error bound. Finally, we investigate under what circumstances
this algorithm can be made efficient. On the last point, given that reasoning
in epistemic logics especially in multi-agent epistemic logics is
PSPACE-complete, it might seem like there is no hope for this problem. We
leverage some recent results on the so-called Representation Theorem explored
for single-agent and multi-agent epistemic logics with the only knowing
operator to reduce modal reasoning to propositional reasoning.

此文介绍了使用多智能体认知逻辑演示 PAC 学习的技术基础，涉及逻辑和弱推理，以提供强大的模型理论框架来回答查询，并研究了该算法的正确性、样本复杂性以及其能够高效的情况，利用表述定理将模态推理纳入命题推理中。

多智体信仰的 PAC 语义学习性

Learnability with PAC Semantics for Multi-agent Beliefs

Mathematical proof aims to deliver confident conclusions, but a very similar
process of deduction can be used to make uncertain estimates that are open to
revision. A key ingredient in such reasoning is the use of a "default" estimate
of $\mathbb{E}[XY] = \mathbb{E}[X] \mathbb{E}[Y]$ in the absence of any
specific information about the correlation between $X$ and $Y$, which we call
*the presumption of independence*. Reasoning based on this heuristic is
commonplace, intuitively compelling, and often quite successful -- but
completely informal.
In this paper we introduce the concept of a heuristic estimator as a
potential formalization of this type of defeasible reasoning. We introduce a
set of intuitively desirable coherence properties for heuristic estimators that
are not satisfied by any existing candidates. Then we present our main open
problem: is there a heuristic estimator that formalizes intuitively valid
applications of the presumption of independence without also accepting spurious
arguments?

该研究介绍了一种启发式估计法和一组启发式估计器的概念，这些估计器并不如现有的候选估计器那样具有直观的一致性属性，最终提出一个重要的开放问题：是否存在一种启发式估计器，既能正式化独立假设的直观有效应用而又不接受错误的论证？

独立假设的形式化

Formalizing the presumption of independence

We design and conduct a simple experiment to study whether neural networks
can perform several steps of approximate reasoning in a fixed dimensional
latent space. The set of rewrites (i.e. transformations) that can be
successfully performed on a statement represents essential semantic features of
the statement. We can compress this information by embedding the formula in a
vector space, such that the vector associated with a statement can be used to
predict whether a statement can be rewritten by other theorems. Predicting the
embedding of a formula generated by some rewrite rule is naturally viewed as
approximate reasoning in the latent space. In order to measure the
effectiveness of this reasoning, we perform approximate deduction sequences in
the latent space and use the resulting embedding to inform the semantic
features of the corresponding formal statement (which is obtained by performing
the corresponding rewrite sequence using real formulas). Our experiments show
that graph neural networks can make non-trivial predictions about the
rewrite-success of statements, even when they propagate predicted latent
representations for several steps. Since our corpus of mathematical formulas
includes a wide variety of mathematical disciplines, this experiment is a
strong indicator for the feasibility of deduction in latent space in general.

通过在固定维度的潜在空间中进行的近似推理的几个步骤，我们证明了神经网络可以对语句进行重写和推理，这对于各种数学学科都是可行的。